11institutetext: Korea Institute of Sciene and Technology, Korea
22institutetext: Korea National University of Science and Technology, Korea
33institutetext: Yonsei-KIST Convergence Research Institute, Yonsei University
33email: {kim1102, gpnam, hskim, haesol<EMAIL_ADDRESS>
# IG-FIQA: Improving Face Image Quality Assessment through Intra-class
Variance Guidance robust to Inaccurate Pseudo-Labels

Minsoo Kim 1122 0000-0001-6813-7335 Gi Pyo Nam 1122 0000-0002-3383-7806 Haksub
Kim 11 0000-0002-8780-9747 Haesol Park 11 0009-0002-7615-6231 Ig-Jae Kim
112233 0000-0002-2741-7047

###### Abstract

In the realm of face image quality assesment (FIQA), method based on sample
relative classification have shown impressive performance. However, the
quality scores used as pseudo-labels assigned from images of classes with low
intra-class variance could be unrelated to the actual quality in this method.
To address this issue, we present IG-FIQA, a novel approach to guide FIQA
training, introducing a weight parameter to alleviate the adverse impact of
these classes. This method involves estimating sample intra-class variance at
each iteration during training, ensuring minimal computational overhead and
straightforward implementation. Furthermore, this paper proposes an on-the-fly
data augmentation methodology for improved generalization performance in FIQA.
On various benchmark datasets, our proposed method, IG-FIQA, achieved novel
state-of-the-art (SOTA) performance.

###### Keywords:

Face image quality assessment, Face recognition

##  1 Introduction

(a) Example of mislabeled pseudo label that generated using conventional SOTA
[4] in the MS1M-V2 dataset. The table lists the min-max normalized scores
measured by various methods on images with yellow boxed.

(b) Illustration of the distribution of the proposed weights used for loss
calculation in the MS1M-V2 dataset. To create an average face image, we
randomly sampled 20 images from each class.

Figure 1: The mislabeling problem existing in conventional SOTA method and our
suggesting solution. Fig. 1(b) depicts our proposed method, which can ignore
classes with low intra-class variance (colored purple) during training, while
classes with high intra-class variance (colored red) are fully utilized for
training the FIQA regression network.

Facial Image Quality Assessment (FIQA) aims to estimate the quality of facial
images for ensuring the reliability of face recognition (FR) algorithms [12].
Unlike traditional image quality assessment methods [24, 21, 34, 2, 38, 25],
which focus on the low-level image characteristics such as brightness,
distortion, and sharpness, FIQA also considers the factors that affect the FR
performance, such as pose variation, facial expression, and occlusion. For
example, a high-resolution facial image with a face mask can receive a high-
quality score in image quality assessment (IQA). However, the same image could
get a lower score in FIQA because the mask interferes with FR.

Recent methods in FIQA can be categorized into two types: methods that propose
computational measurements by analyzing the pre-trained FR feature space, and
methods that predict the FIQ by training regression networks. Among them, the
regression-based methods focus on generating appropriate pseudo labels to
train the FIQA regression network consistently. Various approaches have been
proposed for this purpose, such as manual labeling [1], Wasserstein Distance
(WD) [26], and Certainty Ratio (CR) [4]. Regression models trained using CR
achieve state-of-the-art performance on various benchmarks, demonstrating the
effectiveness of using sample relative classifiability as an approximation for
face image quality. CR is computed by combining the similarity between the
embedding feature and the positive class centroid ($cos(\theta_{y_{i}})$) with
the similarity between the embedding feature and the nearest negative class
centroid $(cos(\theta_{y_{j,j\neq i}}))$. It is designed to have a higher
value when the similarity $cos(\theta_{y_{i}})$ is closer and the similarity
$cos(\theta_{y_{j,j\neq i}})$ is further away.

FIQA method leveraging sample relative classifiability have achieved
remarkable performance but still have limitations. The first limitation is
that pseudo-labels generated from classes with low intra-class variance cannot
accurately reflect the quality of the samples. Typically, when determining
pseudo-labels for image quality, the similarity between the embedding and the
centroid of the corresponding class is utilized. However, in cases where
intra-class variance is low, meaning of consist similar images, a high
similarity is calculated, leading to the generation of incorrect pseudo-labels
regardless of the actual image quality. As seen in Fig. 1(a), even low-
resolution (column 1), low-light (column 2), and occluded (column 3) face
images are assigned higher pseudo-label than high-quality face images (column
4) due to low intra-class variance. This is a common problem because real-
world datasets are often collected from the web [13, 5, 6, 32, 27], and the
removal of noisy data relies on automated methods that use the feature
similarities [7, 40, 9]. As a result, classes with low intra-class variance
may remain in the dataset, and even identical images within a class may exist,
leading to the generation of mislabeled pseudo labels for the FIQA regression
network. Ultimately, this prevents the consistent learning of the regression
network and hinders the model from reaching an optimal solution. Another
limitation of conventional methods is that the training dataset for FR has a
low proportion of low-quality image samples, making it difficult for
regression networks to learn features of low-quality images.

To overcome these limitations, this study proposes two novel approaches to
FIQA training that leverage sample relative classifiability. First, we propose
to identify classes with low intra-class variance while training and assign
them lower weights for the training loss. To identify classes with low intra-
class variance, we utilize the exponential moving average (EMA) of the
distance between the embedding and the prototype as an approximation of intra-
class variance. The proposed method can effectively measure intra-class
variance while requiring negligible computational resources and has the
advantage of not requiring a pre-trained FR model. Second, we propose a novel
and effective method to boost FIQA regression networks through on-the-fly data
augmentation. The proposed method, IG-FIQA, leverages on-the-fly image
rescaling, random erasing, and color jittering on training images, allowing
the FIQA model to learn factors that may interfere with FR. This type of
augmentation method poses the risk of impairing the performance of the FR
model [18], potentially resulting in the generation of inaccurate pseudo-
labels. Therefore, we have designed a method that safely incorporates data
augmentation exclusively for FIQA regression network training. Our
contributions can be summarized as follows:

  * $\bullet$

This paper introduces a novel approach to weight loss by incorporating Intra-
class variance Guidance. This prevents the regression network from learning
incorrect information through inappropriate pseudo-labels.

  * $\bullet$

We propose a novel and effective method to boost FIQA regression networks via
on-the-fly data augmentation to consider a variety of real face images.

  * $\bullet$

IG-FIQA enables robust FIQA training and achieves state-of-the-art results on
various benchmarks.

##  2 Related work

Existing FIQA methods can be classified into two types. One is to use
embedding’s properties, and the other is to predict face image quality using a
regression network.

###  2.1 Embedding’s property based methods

The embedding’s properties based methods estimate the FIQ score by leveraging
characteristics within the feature space or properties inherent to the facial
recognition (FR) model. Probabilistic Face Embeddings (PFEs) [30] proposed the
method to represent a embedding as a Gaussian distribution in the latent
space, where the mean of the distribution estimates the most likely feature
values while the variance shows the uncertainty in the feature values. SER-FIQ
[31] estimated the face image quality by calculating the distance between
multiple embeddings on a query image, which were produced by different random
subnetworks of the backbone. [23] and [18] suggested a method to utilize the
magnitude of embedding as an FIQ score, which is extracted from FR models
trained with softmax-variant loss. [10] found that the feature distance
between unrecognizable identity clusters and queries was correlated with the
quality of face images, and used this distance as a FIQ score.

###  2.2 Regression based methods

Regression based FIQA approaches aim to train the regression network directly
for predicting FIQ scores, unlike embedding property-based methods that do not
require additional training. Given the absence of ground-truth data for face
image quality, most methods within this approach aim to generate accurate
pseudo-labels for image quality, facilitating the reliable training of
regression networks. One easily devised method to obtain pseudo-labels is
manual assignment by humans [1]. FaceQnet [15] proposed using the euclidean
distance between the best quality image in the class and the target image as a
pseudo-label. PCNet [36] learned a face recognizer using only half of the
dataset, then used half of the remaining dataset to construct a mated pair,
and used the cosine similarity between pairs as a pseudo-label. SDD-FIQA [26]
proposed to use the distance between the intra-class similarity distribution
and the inter-class similarity distribution as pseudo-labels. CR-FIQA [4]
proposed a method that utilizes the classifiability of embeddings as a pseudo-
label. The network trained with pseudo-labels generated using classifiability
has demonstrated its excellence by achieving state-of-the-art performance in
various benchmarks.

##  3 Methodology

In this section, we explain the concepts and limitations of conventional SOTA
method briefly and provide details of the proposed method to overcome these
limitations by selectively assigning lower weight to samples belonging to
classes with low intra-class variance and utilizing data augmentation.

###  3.1 Revisiting CR-FIQA

CR-FIQA [4] is a FIQA method that utilizes relative classifiability as a
pseudo-label for the FIQ score. In order to mathematically define
classifiability, CR-FIQA introduces two novel concepts: Class Center
Similarity (CCS) and Negative Nearest Class Center Similarity (NNCCS),

$\displaystyle\mathrm{CCS}_{x_{i}}$ $\displaystyle=cos(\theta_{y_{i}}),$ (1)
$\displaystyle\mathrm{NNCCS}_{x_{i}}$
$\displaystyle=\max_{j\in\left\\{1,...,C\right\\},j\neq
y_{i}}cos(\theta_{j}),$

where $C$ represents the total number of classes in the training dataset,
while $y_{i}$ denotes the ground truth label corresponding to sample $x_{i}$.
$\theta_{y_{i}}$ is the angle between the embedding $f(x_{i})$ extracted from
the backbone network and the prototype $W_{y_{i}}$. $\mathrm{CCS}$ measures
how similar an embedding is to the prototype of its corresponding class using
cosine similarity, while $\mathrm{NNCCS}$ measure the similarity between an
embedding and the prototype of the nearest negative class. Utilizing these two
concepts, pseudo-labels to train a regression network is defined as follows:

$\mathrm{CR}_{x_{i}}=\frac{\mathrm{CCS}_{x_{i}}}{\mathrm{NNCCS}_{x_{i}}+(1+\epsilon)},$
(2)

where $\epsilon$ is set to $1e-9$ to prevent division by zero. According to
its definition, a $\mathrm{CR}_{x_{i}}$ value increases as an embedding
approaches the positive class prototype and diverges from prototypes of
nearest negative classes. Consequently, a higher $\mathrm{CR}_{x_{i}}$ value
implies that the sample $x_{i}$ is more easily classifiable.

CR-FIQA employs $\mathrm{CR}$ as pseudo-labels to train a regression network,
$R\in D\times 1$, which consists of a single linear layer taking the embedding
feature with dimension $D$ from the FR backbone as input. Smooth L1 loss was
used to avoid gradient explosion. For each sample, its loss is defined as,

$l_{\mathrm{CR}}\left(x_{i}\right)=\left\\{\begin{matrix}0.5/\beta\times\left(d_{\mathrm{CR}}\left(x_{i}\right)\right)^{2}&{\scriptstyle\text{if
}\left|d_{\mathrm{CR}}\left(x_{i}\right)\right|<\beta}\\\
\left|d_{\mathrm{CR}}\left(x_{i}\right)\right|-0.5\times\beta&{\scriptstyle\text{otherwise}}\\\
\end{matrix}\right.,\\\ $ (3)

where
$d_{\mathrm{CR}}\left(x_{i}\right)=\mathrm{CR}_{x_{i}}-R\left(f\left(x_{i}\right)\right)$.
The total loss for training CR-FIQA is a combination of $L_{\mathrm{Arc}}$,
which trains the backbone network as in [8], and $L_{\mathrm{CR}}$, which
trains the regression network:

$L_{\mathrm{CR}}=\sum_{i}l_{\mathrm{CR}}\left(x_{i}\right),$ (4)
$L_{\mathrm{CR-FIQA}}=L_{\mathrm{Arc}}+\lambda\times L_{\mathrm{CR}}.$ (5)

$\lambda$ is weight parameter to balance angular margin loss
$L_{\mathrm{Arc}}$ and regression loss $L_{\mathrm{CR}}$, and set to 10.0 in
CR-FIQA.

Figure 2:  An overview of IG-FIQA training process. IG-FIQA utilizes an
approximation of the intra-class variance to handle the adverse effects of
samples with low intra-class variance. Note that the weight parameter $v_{i}$
does not require gradient updates during training. Original image forward
pass: Blue, augmented image forward pass: Green.

###  3.2 IG-FIQA

Mitigating the impact of low intra-class variance. To handle the impact of
images with low intra-class variance on the pseudo-labels for FIQ, we
investigate approaches to identify classes exhibiting low intra-class variance
during training. This process involves computing intra-class variance [28],
defined as follows:

$\displaystyle var_{y_{i}}$
$\displaystyle=\frac{1}{N}\sum_{i}^{N}\left\|f(x_{i})-\mu_{f(x)}\right\|^{2},$
(6) $\displaystyle\mu_{f(x)}$ $\displaystyle=\frac{1}{N}\sum_{i}^{N}f(x_{i}),$

where $N$ represents the number of samples in the class $y_{i}$. Computing
$var_{y_{i}}$ at every iteration is a straightforward approach to identify
classes with low intra-class variance. However, calculating the class variance
every iteration with this primitive method is highly inefficient and
practically infeasible. For efficient computation, we propose a method to
approximate $var_{y_{i}}$ and $\mu_{f(x)}$ fairly accurately. Firstly, we
leverage the observation that as training progresses, prototype $W_{y_{i}}$
converges to the class centroid $\mu_{f(x)}$. At this point,
$\left\|f(x_{i})-\mu_{f(x)}\right\|^{2}$ term in Eq. 6 could be approximated
by $1-\mathrm{CCS}_{x_{i}}$. With this approximation, $var_{y_{i}}$ can be
represented as follows:

$var_{y_{i}}\approx\frac{1}{N}\sum_{i}^{N}1-\mathrm{CCS}_{x_{i}},$ (7)

With the suggested approximation Eq. 7, we no longer need to calculate
$\mu_{f(x)}$ in order to compute the class variance. To further simplify the
calculation of the average of $1-\mathrm{CCS}_{x_{i}}$, we utilized the
exponential moving average (EMA). By this, the intra-class variance
$v_{y_{i}}^{t}$ for class $y_{i}$ at the $t$ th iteration can be represented
by the following:

$var_{y_{i}}\approx v_{y_{i}}^{t}=\alpha\times
v_{y_{i}}^{t-1}+(1-\alpha)\times(1-\mathrm{CCS}_{x_{i}}),$ (8)

where $\alpha$ is a momentum hyperparameter. If $\alpha$ is small,
$var_{y_{i}}$ can be greatly affected by $\mathrm{CCS}_{x_{i}}$, and
conversely, if $\alpha$ is small, $var_{y_{i}}$ will be affected less. The
$\mathrm{CCS}$ values undergo significant fluctuations in the early stages of
learning since the model parameters has not fully converged, while the
variation becomes minor in the later stages of training. For this reason, we
gradually increased the $\alpha$ from 0.9 to 1.0 until the last epoch
$e_{end}$ of training. Detailed experiment and analysis for hyperparameter
$\alpha$ is descibed in the ablation study 4.2. Since $\mathrm{CCS}_{x_{i}}$
should be computed at every iteration to generate $\mathrm{CR}_{x_{i}}$, the
proposed method has negligible computational burden on computing $v_{y_{i}}$.

Afterwards, $v_{y_{i}}$’s are adjusted to the range of [0, 1] through z-score
normalization:

$\left\|\hat{v_{y_{i}}}\right\|=1+\left\lfloor\frac{v_{y_{i}}-\mu_{v}}{\sigma_{v}}\right\rceil_{-1}^{0},$
(9)

where $\mu_{v}$ and $\sigma_{v}$ are the mean and standard deviation of
$v_{y_{i}}$ computed across all classes, respectively. As a result,
$\left\|\hat{v_{y_{i}}}\right\|$ is intended to be $1$ when the class $y_{i}$
comprises diverse images, and tends towards $0$ in the case of homogeneous and
similar images. This value of $\left\|\hat{v_{y_{i}}}\right\|$ serves as the
weight parameter for $L_{CR}$ during regression network training. Since
approximately 16$\%$ of the unit gaussian distribution has values less than
-1, IG-FIQA trains using only classes with intra-class variance in the top
$84\%$.

$L_{\mathrm{IG}}=\sum_{i}\left\|\hat{v_{y_{i}}}\right\|\times
l_{\mathrm{CR}}(x_{i}),$ (10)

$v_{y_{i}}$’s are initialized to 1.0 for all classes at the beginning of
training so that all data samples could equally contribute to training. The
overall loss of the proposed method can be represented as follows:

$L_{\mathrm{IG-FIQA}}=L_{\mathrm{Arc}}+\lambda\times L_{\mathrm{IG}}.$ (11)

We set $\lambda$ to 10.0, following the original CR-FIQA. Further experiments
detailed in 4.2 demonstrate that the proposed method can calculate the class
variance fairly accurately, taking only 0.7 seconds per iteration, whereas the
naive approach took 58.7 seconds on RTX3090 with the CASIA-WebFace dataset and
a mini-batch size of 1024.

Boosting FIQA through data augmentation. The proposed IG-FIQA applies
rescaling, random erasing, and color jittering as data augmentations that
could degrade the face image quality. This augmentation improves the model’s
adaptability to a variety of low-quality facial images that could exist in the
unconstrained real world, such as images acquired from CCTV. However, using
heavily degraded face images for training runs the risk of overfitting the FR
backbone to extract features from non-face information, which may ultimately
hinder the FR backbone training [18]. For this reason, we do not utilize the
augmented data for $L_{\mathrm{Arc}}$ calculation but use it for calculating
the loss only for the regression network. This is achieved through a simple
mini-batch separation, allocating one part for the regression network and the
other for backbone network training. Our pipeline is specifically designed to
ensure that augmentations degrading image quality are excluded from the batch
forwarded to the FR backbone network, thereby preserving the integrity of the
training process. The complete forward pass and backward pass of the proposed
method can be seen in the overview Fig. 2. As shown in the overview, quality-
degraded facial images are only used for calculating the regression loss
$L_{\mathrm{IG}}$ and generating the pseudo-label $\mathrm{CR}{x_{i}}$.

(a) Examples of facial image in the benchmark dataset.

(b) Quality score distribution of benchmark datasets.

Figure 3: In Fig. 3(a), pictures in the same row belong to the same ID. The
quality scores for Fig. 3(b) were obtained using IG-FIQA and have been
normalized to [0, 1].

##  4 Experiments and Results

###  4.1 Implementation Details

Datasets. We utilized the CASIA-WebFace [37] and MS1M-V2 [8] datasets for
training. For evaluation, we employed the LFW [16], CFP-FP [29], CPLFW [39],
XQLFW [19], IJB-B [35], and IJB-C [22] datasets. All images used in training
and evaluation were cropped and aligned to a size of $112\times 112$ pixels as
specified in [20, 33]. While LFW, CFP-FP, and CPLFW are extensively used
benchmarks, the performance of FR models has reached a saturation point due to
the predominance of high-quality images in these datasets [18]. On the other
hand, XQLFW, IJB-B, and IJB-C are benchmarks consisting of a mixture of high-
quality and low-quality images. This indicates that they are suitable datasets
for evaluating FIQA performance. Overall, these evaluation datasets cover
various challenges for FR, including variations in pose, illumination, and
resolution. For a better understanding of the image qualities within the
benchmarks, we plot the FIQA score distribution using IG-FIQA in Fig. 3(b).
Examples of facial image quality for LFW, CFP-FP, XQLFW, and IJB-C can be
found in Fig. 3(a).

Experiment Settings. Similar to CR-FIQA, the performance evaluation of the
proposed IG-FIQA was conducted under two distinct protocols: a small protocol
(IG-FIQA(S)) and a large protocol (IG-FIQA(L)). In IG-FIQA(S), we utilized
ResNet-50 as the backbone and the CASIA-WebFace as training dataset. We set
the initial learning rate to 1e-1, divide the learning rate by 10 at 20 and 28
epochs, and end training after 36 epochs. For IG-FIQA(L), ResNet-100 used as
the backbone and MS1M-V2 used as the training dataset. The initial learning
rate was set to 1e-1 and divided by 10 in 10 and 16 epochs, and training was
ended after 20 epochs. For both protocols, the SGD optimizer with a momentum
of 0.9 and weight decay of 5e-4 was employed. Regarding the ArcFace loss, the
scale parameter (s) and the margin (m) remained at 64.0 and 0.5, respectively,
following the specifications from the original paper [8]. We set the mini-
batch size to 1024, with 512 images for regression network training and the
remaining 512 images for FR backbone training.

Evaluation metrics. The Error versus Rejection Curve (ERC), which is the most
common method for measuring FIQA performance [11, 12], was used to compare the
performance with recent SOTA FIQA methods. It measures verification
performance through False None Match Rate (FNMR) based on the rejection rate
of the quality score at a fixed False Match Rate (FMR). Additionally, we
reported the Area Under Curve (AUC) of ERC in Tab. 2 to evaluate verification
performance across all rejection rate intervals of the ERC. A smaller AUC
value indicates better performance of the FIQA model. All the experimental
results presented in this paper were obtained under cross-model settings; the
FIQA models were solely employed to assess the quality of face images, and the
embedding features were extracted using independent pre-trained FR models.

Augmentations. In this paper, we simply adopt rescaling, random erasing, and
color jittering as augmentations to train the FIQA regression network. These
methods are commonly used to train classification networks and intentionally
degrade image quality [14]. Specifically, rescaling involved shrinking the
image and then restoring it to the original size, resulting in blurring of the
face image. For random erasing, we randomly selected a rectangular area from
the sample and set its pixel values to 0. Color jittering randomly modified
the brightness, contrast, and saturation of the image. Additionally, random
horizontal flip was applied to both mini-batches forwarded to the FR backbone
and regression network training, as it does not degrade the image quality.

Face Recognition Models. In the experiment, we used six commonly used models
for FR: CosFace [33], ArcFace [8], CurricularFace [17], MagFace [23],
ElasticFace [3] and AdaFace [18], all trained with ResNet-100 as the backbone
using MS1M-V2 dataset. In our experiments, all FR models utilized pre-trained
weights available in the official repository, except for CosFace [33] and
ArcFace [8], which were re-implemented due to the lack of pre-trained models
under the same conditions.

(a) Changes of $\mathrm{CCS}_{\mathrm{dist}}$ during training.

(b) Changes of pearson correlation $\rho_{var,v}$ during training.

Figure 4: Ablation study on momentum paramter $\alpha$.

###  4.2 Ablation and analysis

Effect of momentum paramter $\alpha$. The hyperparameter $\alpha$ is an
important factor that determines how much the $\mathrm{CCS}_{x_{i}}$ in the
current iteration influences the weight parameter $\left\|\hat{v}\right\|$.
More specifically, the influence of the $\mathrm{CCS}_{x_{i}}$ in the current
iteration on the weight parameter is inversely proportional to the $\alpha$.
To determine an appropriate setting for the $\alpha$, we tracked the average
change of $\mathrm{CCS}_{x_{i}}$ ($\mathrm{CCS}_{\mathrm{dist}}$) throughout
each epoch of the training process, as follows:

$\mathrm{CCS}_{\mathrm{dist}}=\frac{1}{k}\sum_{i=1}^{k}\left|\mathrm{CCS}_{x_{i},e_{t}}-\mathrm{CCS}_{x_{i},e_{t-1}}\right|,$
(12)

where $\mathrm{CCS}_{x_{i},e_{t}}$ refers to the $\mathrm{CCS}$ obtained using
the $x_{i}$ embedding after the $t$ th epoch $e_{t}$. As shown in Fig. 4(a),
$\mathrm{CCS}_{\mathrm{dist}}$ is large in the early stages of training, but
gradually decreases as the model converges. In order to quickly reflect
changing CCS values in the weight parameters, it is more advantageous to use a
low momentum parameter. Conversely, in the later stages of learning, it is
reasonable to design the weight parameter to be less affected by the instance
$\mathrm{CCS}_{x_{i}}$ by using a high $\alpha$. For this reason, we use low
momentum parameters at the beginning of training and gradually increase the
momentum parameters to 1.0 until the end of training.

To verify the efficacy of the proposed variable $v_{y_{i}}$, we calculated the
correlation between $v_{y_{i}}$ and the intra-class variance $var_{y_{i}}$
during training. Intra-class variance $var_{y_{i}}$ is computed with
pretrained ResNet-50 ArcFace model. Fig. 4(b) shows the changes in the Pearson
correlation coefficient $\rho_{var,v}$ between $var_{y_{i}}$ and $v_{y_{i}}$.
As depicted in the figure, the correlation between the two variables is
maximized when the momentum parameter $\alpha$ gradually increases from 0.9 to
1.0 during training. We also observed that the Pearson correlation between
$var_{y_{i}}$ and the proposed $v_{y_{i}}$ reaches a high value ($>0.71$) from
the end of the second training epoch. This indicates that the proposed method
measures intra-class variance fairly accurately from the early stage of
training and reflects it in regression network training.

Figure 5:  ERC plots comparing conventional SOTA method, our method without
augmentation (IG-FIQA(S-)), and our method with augmentation (IG-FIQA(S)).

Effect of $L_{\mathrm{IG}}$ loss. To validate the effectiveness of the
proposed weight parameter, we plotted the ERC of IG-FIQA(S) without
augmentation, IG-FIQA(S) with data augmentation, and CR-FIQA(S) in Fig. 5. As
shown in the figure, we can see that even without data augmentation, IG-
FIQA(S) outperforms CR-FIQA(S) on both high-quality and mixed-quality
datasets. This result shows that ignoring classes with low intra-class
variance during training is effective for model generalization. In Fig. 1(b),
we plot the distribution of the weight parameter $\left\|\hat{v}\right\|$
assigned to each class in MS1M-V2 dataset after training. Below the
distribution, we present the average face image of the class corresponding to
the distribution. As can be seen in the Fig. 1(b), 16% of classes with
$\left\|\hat{v}\right\|$ equal to 0 are ignored for the training of the
regression network. The average facial image derived from these classes looks
like a single image, due to low intra-class variance.

FR | Data-aug | LFW | CFP-FP | CPLFW | XQLFW | IJB-B | IJB-C
---|---|---|---|---|---|---|---
ArcFace | - | 0.0016 | 0.0070 | 0.0396 | 0.2339 | 0.0268 | 0.0177
20% | 0.0017 | 0.0083 | 0.0396 | 0.2206 | 0.0246 | 0.0168
30% | 0.0017 | 0.0077 | 0.0374 | 0.2059 | 0.0245 | 0.0166
40% | 0.0017 | 0.0081 | 0.0387 | 0.2147 | 0.0246 | 0.0164
AdaFace | - | 0.0019 | 0.0091 | 0.0341 | 0.1708 | 0.0225 | 0.0143
20% | 0.0017 | 0.0099 | 0.0341 | 0.1522 | 0.0205 | 0.0133
30% | 0.0018 | 0.0098 | 0.0323 | 0.1432 | 0.0205 | 0.0132
40% | 0.0018 | 0.0094 | 0.0336 | 0.1434 | 0.0208 | 0.0132

(a) The AUCs of ERCs in FMR=1e-3, according to augmentation ratio. Red is the
best.

Methods | Data-aug | LFW | CFP-FP | CPLFW | XQLFW | IJB-B | IJB-C
---|---|---|---|---|---|---|---
CR-FIQA(S) | - | 99.35 | 96.59 | 85.30 | 66.23 | 77.72 | 82.10
CR-FIQA(S) | ✓ | 98.63 | 82.10 | 75.70 | 67.70 | 64.75 | 63.21
IG-FIQA(S) | - | 99.35 | 96.04 | 86.00 | 69.45 | 83.07 | 85.19
IG-FIQA(S) | ✓ | 99.38 | 96.37 | 86.90 | 68.22 | 82.27 | 85.96

(b) Performance degradation of FR backbones depending on data augmentation.
Verification accuracy for IJB-B and IJB-C are reported on TAR@FAR=1e-3.

Table 1: Ablation study for augmentations.

Ablation study on augmentation. To find the optimal data augmentation ratio,
we trained the IG-FIQA(S) using various data augmentation ratios. Tab. 1(a)
shows the AUC of ERC evaluated on various benchmarks at FMR=1e-3. As seen in
the table, we observed that the model trained using an augmentation ratio of
$30\%$ achieved the best performance in most cases. Based on this experiment,
we applied rescaling, random erasing, and color jittering at a rate of $30\%$
probability each, resulting only 34.3% $(0.7^{3})$ of images statistically not
undergoing any augmentation during training. As can be seen in Fig. 5, IG-
FIQA(S) with data augmentation achieves a further performance improvement than
its non-augmented counterpart, especially in mixed-quality benchmarks.

To demonstrate that the suggested pipeline effectively protects the FR
backbone from the risk of degradation due to augmentation, we measured the FR
verification accuracy of the trained FIQA backbone in Tab. 1(b) using FR
benchmarks. As observed in the table, the proposed separated pipeline method
enabled stable backbone training regardless of augmentation, while the
original CR-FIQA backbone suffered performance degradation in most benchmarks
due to data augmentations. This indicates that the proposed pipeline, with
batch separation, can effectively prevent performance degradation caused by
data augmentations, thereby ensuring stable training of the FR backbone, which
is necessary for accurate pseudo-label generation.

Figure 6:  ERC plots on ArcFace.  Figure 7:  ERC plots on AdaFace.

FR | Method | High-quality | Mixed-quality
---|---|---|---
LFW | CFP-FP | CPLFW | XQLFW | IJB-B | IJB-C
1e-3 | 1e-4 | 1e-3 | 1e-4 | 1e-3 | 1e-4 | 1e-3 | 1e-4 | 1e-3 | 1e-4 | 1e-3 | 1e-4
CosFace† | SER-FIQ | 0.0021 | 0.0025 | 0.0100 | 0.0183 | 0.0404 | 0.0462 | 0.2128 | 0.2580 | 0.0246 | 0.0370 | 0.0166 | 0.0272
SDD-FIQA | 0.0012 | 0.0020 | 0.0096 | 0.0185 | 0.0464 | 0.0518 | 0.2607 | 0.2937 | 0.0297 | 0.0447 | 0.0196 | 0.0310
MagFace | 0.0014 | 0.0019 | 0.0096 | 0.0169 | 0.0496 | 0.0545 | 0.3997 | 0.4823 | 0.0272 | 0.0414 | 0.0181 | 0.0290
CR-FIQA(S) | 0.0016 | 0.0023 | 0.0098 | 0.0240 | 0.0412 | 0.0472 | 0.2331 | 0.3156 | 0.0306 | 0.0460 | 0.0198 | 0.0318
CR-FIQA(L) | 0.0017 | 0.0022 | 0.0075 | 0.0130 | 0.0373 | 0.0429 | 0.2133 | 0.2466 | 0.0245 | 0.0371 | 0.0159 | 0.0258
IG-FIQA(S-)(Our) | 0.0016 | 0.0022 | 0.0097 | 0.0213 | 0.0402 | 0.0461 | 0.2418 | 0.2962 | 0.0294 | 0.0443 | 0.0188 | 0.0303
IG-FIQA(S)(Our) | 0.0015 | 0.0022 | 0.0100 | 0.0210 | 0.0375 | 0.0433 | 0.2179 | 0.2560 | 0.0270 | 0.0403 | 0.0177 | 0.0280
IG-FIQA(L)(Our) | 0.0013 | 0.0018 | 0.0070 | 0.0121 | 0.0374 | 0.0427 | 0.2124 | 0.2387 | 0.0240 | 0.0364 | 0.0155 | 0.0255
ArcFace† | SER-FIQ | 0.0023 | 0.0028 | 0.0069 | 0.0085 | 0.0390 | 0.0439 | 0.1947 | 0.2347 | 0.0219 | 0.0330 | 0.0156 | 0.0235
SDD-FIQA | 0.0013 | 0.0020 | 0.0077 | 0.0098 | 0.0468 | 0.0504 | 0.2649 | 0.2930 | 0.0270 | 0.0383 | 0.0185 | 0.0267
MagFace | 0.0017 | 0.0022 | 0.0074 | 0.0091 | 0.0495 | 0.0532 | 0.3730 | 0.3996 | 0.0247 | 0.0355 | 0.0171 | 0.0251
CR-FIQA(S) | 0.0017 | 0.0023 | 0.0091 | 0.0111 | 0.0409 | 0.0460 | 0.2384 | 0.2757 | 0.0275 | 0.0398 | 0.0185 | 0.0272
CR-FIQA(L) | 0.0018 | 0.0024 | 0.0050 | 0.0062 | 0.0371 | 0.0410 | 0.2055 | 0.2538 | 0.0222 | 0.0328 | 0.0149 | 0.0225
IG-FIQA(S-)(Our) | 0.0016 | 0.0022 | 0.0070 | 0.0097 | 0.0396 | 0.0437 | 0.2339 | 0.2688 | 0.0268 | 0.0383 | 0.0177 | 0.0258
IG-FIQA(S)(Our) | 0.0017 | 0.0022 | 0.0077 | 0.0101 | 0.0374 | 0.0415 | 0.2059 | 0.2390 | 0.0245 | 0.0351 | 0.0166 | 0.0241
IG-FIQA(L)(Our) | 0.0016 | 0.0022 | 0.0052 | 0.0063 | 0.0371 | 0.0407 | 0.1940 | 0.2405 | 0.0217 | 0.0316 | 0.0146 | 0.0217
CurricularFace | SER-FIQ | 0.0024 | 0.0028 | 0.0092 | 0.0122 | 0.0345 | 0.0574 | 0.1664 | 0.1969 | 0.0223 | 0.0332 | 0.0156 | 0.0238
SDD-FIQA | 0.0016 | 0.0022 | 0.0112 | 0.0144 | 0.0413 | 0.0664 | 0.2320 | 0.2613 | 0.0271 | 0.0389 | 0.0184 | 0.0273
MagFace | 0.0017 | 0.0022 | 0.0098 | 0.0122 | 0.0448 | 0.0666 | 0.3543 | 0.3966 | 0.0253 | 0.0358 | 0.0174 | 0.0254
CR-FIQA(S) | 0.0021 | 0.0027 | 0.0120 | 0.0150 | 0.0350 | 0.0586 | 0.2162 | 0.2585 | 0.0279 | 0.0408 | 0.0185 | 0.0276
CR-FIQA(L) | 0.0023 | 0.0029 | 0.0071 | 0.0090 | 0.0330 | 0.0507 | 0.1762 | 0.2579 | 0.0226 | 0.0332 | 0.0151 | 0.0230
IG-FIQA(S-)(Our) | 0.0019 | 0.0023 | 0.0112 | 0.0143 | 0.0348 | 0.0596 | 0.2145 | 0.2586 | 0.0269 | 0.0389 | 0.0175 | 0.0262
IG-FIQA(S)(Our) | 0.0018 | 0.0022 | 0.0100 | 0.0133 | 0.0330 | 0.0540 | 0.1680 | 0.2086 | 0.0249 | 0.0360 | 0.0166 | 0.0248
IG-FIQA(L)(Our) | 0.0017 | 0.0022 | 0.0071 | 0.0095 | 0.0329 | 0.0532 | 0.1692 | 0.2239 | 0.0222 | 0.0323 | 0.0148 | 0.0224
MagFace | SER-FIQ | 0.0024 | 0.0028 | 0.0088 | 0.0094 | 0.0382 | 0.0673 | 0.1804 | 0.2241 | 0.0218 | 0.0302 | 0.0148 | 0.0211
SDD-FIQA | 0.0016 | 0.0023 | 0.0107 | 0.0122 | 0.0446 | 0.0901 | 0.2414 | 0.2852 | 0.0268 | 0.0372 | 0.0177 | 0.0249
MagFace | 0.0017 | 0.0023 | 0.0084 | 0.0097 | 0.0481 | 0.0736 | 0.3552 | 0.4023 | 0.0247 | 0.0343 | 0.0164 | 0.0232
CR-FIQA(S) | 0.0020 | 0.0029 | 0.0108 | 0.0142 | 0.0386 | 0.0651 | 0.2175 | 0.2504 | 0.0282 | 0.0388 | 0.0182 | 0.0253
CR-FIQA(L) | 0.0022 | 0.0028 | 0.0055 | 0.0069 | 0.0358 | 0.0502 | 0.1852 | 0.2136 | 0.0222 | 0.0309 | 0.0144 | 0.0205
IG-FIQA(S-)(Our) | 0.0019 | 0.0026 | 0.0084 | 0.0116 | 0.0378 | 0.0747 | 0.2103 | 0.2280 | 0.0271 | 0.0370 | 0.0173 | 0.0240
IG-FIQA(S)(Our) | 0.0018 | 0.0024 | 0.0092 | 0.0112 | 0.0357 | 0.0610 | 0.1827 | 0.2021 | 0.0247 | 0.0336 | 0.0161 | 0.0222
IG-FIQA(L)(Our) | 0.0017 | 0.0022 | 0.0059 | 0.0071 | 0.0360 | 0.0609 | 0.1747 | 0.2133 | 0.0217 | 0.0298 | 0.0140 | 0.0197
ElasticFace | SER-FIQ | 0.0021 | 0.0025 | 0.0071 | 0.0132 | 0.0391 | 0.0569 | 0.1678 | 0.2029 | 0.0234 | 0.0337 | 0.0164 | 0.0249
SDD-FIQA | 0.0012 | 0.0017 | 0.0079 | 0.0121 | 0.0446 | 0.0607 | 0.2572 | 0.3127 | 0.0286 | 0.0406 | 0.0196 | 0.0287
MagFace | 0.0014 | 0.0019 | 0.0071 | 0.0133 | 0.0483 | 0.0643 | 0.3675 | 0.4249 | 0.0262 | 0.0368 | 0.0182 | 0.0263
CR-FIQA(S) | 0.0016 | 0.0021 | 0.0087 | 0.0139 | 0.0391 | 0.0581 | 0.2214 | 0.2901 | 0.0296 | 0.0418 | 0.0199 | 0.0289
CR-FIQA(L) | 0.0017 | 0.0022 | 0.0056 | 0.0096 | 0.0358 | 0.0517 | 0.1719 | 0.2012 | 0.0238 | 0.0337 | 0.0161 | 0.0235
IG-FIQA(S-)(Our) | 0.0015 | 0.0020 | 0.0069 | 0.0122 | 0.0378 | 0.0551 | 0.2216 | 0.2566 | 0.0286 | 0.0395 | 0.0188 | 0.0271
IG-FIQA(S)(Our) | 0.0015 | 0.0020 | 0.0066 | 0.0121 | 0.0356 | 0.0524 | 0.1772 | 0.2033 | 0.0266 | 0.0366 | 0.0179 | 0.0255
IG-FIQA(L)(Our) | 0.0013 | 0.0018 | 0.0053 | 0.0094 | 0.0355 | 0.0512 | 0.1631 | 0.1908 | 0.0234 | 0.0329 | 0.0158 | 0.0231
AdaFace | SER-FIQ | 0.0024 | 0.0028 | 0.0081 | 0.0129 | 0.0329 | 0.0389 | 0.1312 | 0.1785 | 0.0181 | 0.0257 | 0.0121 | 0.0176
SDD-FIQA | 0.0016 | 0.0022 | 0.0096 | 0.0162 | 0.0409 | 0.0469 | 0.1911 | 0.2646 | 0.0220 | 0.0313 | 0.0147 | 0.0210
MagFace | 0.0017 | 0.0022 | 0.0079 | 0.0125 | 0.0443 | 0.0498 | 0.3107 | 0.3352 | 0.0205 | 0.0289 | 0.0136 | 0.0193
CR-FIQA(S) | 0.0021 | 0.0026 | 0.0107 | 0.0171 | 0.0347 | 0.0410 | 0.1798 | 0.2188 | 0.0229 | 0.0322 | 0.0146 | 0.0208
CR-FIQA(L) | 0.0023 | 0.0028 | 0.0062 | 0.0097 | 0.0322 | 0.0377 | 0.1542 | 0.2126 | 0.0183 | 0.0262 | 0.0119 | 0.0171
IG-FIQA(S-)(Our) | 0.0019 | 0.0023 | 0.0091 | 0.0146 | 0.0341 | 0.0400 | 0.1708 | 0.2107 | 0.0225 | 0.0313 | 0.0143 | 0.0200
IG-FIQA(S)(Our) | 0.0018 | 0.0022 | 0.0098 | 0.0164 | 0.0323 | 0.0379 | 0.1432 | 0.1890 | 0.0205 | 0.0283 | 0.0132 | 0.0186
IG-FIQA(L)(Our) | 0.0017 | 0.0022 | 0.0062 | 0.0096 | 0.0322 | 0.0377 | 0.1268 | 0.1712 | 0.0180 | 0.0254 | 0.0116 | 0.0165

Table 2: AUCs of ERC obtained by recent SOTA FIQA methods and suggesting IG-
FIQA. Annotated † means re-implemented, and without annotation means used
pretrained model provided from official repository. IG-FIQA(S-) refers to the
IG-FIQA(S) without augmentation. Red : best, Blue : second.

###  4.3 Comparison with SOTA methods

We compared the FIQA performance of the proposed model, IG-FIQA, with the
recently presented SOTA models: SER-FIQ [31], SDD-FIQA [26], MagFace [23], and
CR-FIQA [4]. In Tab. 2, we annotated the AUCs as verification performance at
FMR=1e-3 and FMR=1e-4. ERCs using ArcFace and AdaFace are reported in Fig. 6
and Fig. 7. For a more detailed analysis of the impact of data augmentation,
we also include a small protocol model trained without data augmentation (IG-
FIQA(S-)) in the results.

From Fig. 6 and Fig. 7, we can see that all FIQA methods fluctuate on the LFW
benchmark and can not infer quality properly. This is because current SOTA FR
models have already reached saturation in the LFW benchmark; in other words,
FR models can extract feature robustly while ignoring minor quality
degradation. Since IG-FIQA uses data augmentation to generate images of
various qualities and uses them for training, there is a risk of poor
performance on high-quality benchmarks compared to other FIQA models trained
only on high-quality datasets. In fact, it can be seen that IG-FIQA(S-)
performs better than IG-FIQA(S) in CFP-FP benchmark. Nevertheless, the
proposed IG-FIQA achieved similar or slightly better performance than the
conventional SOTA methods in the CPLFW and CFP-FP benchmarks.

From a FIQA perspective, the original purpose of FIQA is to select good
quality facial images from multiple mixed-quality images to ensure reliable FR
algorithm performance. However, with the emergence of high-performance FR
models, FR performance on high-quality datasets has become saturated.
Therefore, FIQA is less necessary for high-quality datasets, and it can be
difficult to distinguish between superior FIQA methods. Noteworthily, IG-FIQA
outperforms most SOTA models on the mixed-quality benchmark datasets. This
indicates that the proposed IG-FIQA is an effective FIQA model capable of
filtering low-quality images from images of varying qualities, aligning with
the original purpose of FIQA. Verification pairs for the XQLFW benchmark are
selected based on SER-FIQ and BRISQUE [24] scores, which may give an advantage
to SER-FIQ. Nevertheless, IG-FIQA outperforms SER-FIQ on XQLFW and achieves
SOTA. Additionally, the performance gap between small and large protocols of
the proposed method is much smaller on various benchmarks than that of CR-
FIQA. This means that IG-FIQA is capable of generalizing the regression
network effectively, even with small training datasets and a lightweight FR
backbone. Our small protocol model without augmentation (IG-FIQA(S-))
consistently exhibits better performance than CR-FIQA(S). This proves that the
proposed method of removing classes that are at risk of being mislabeled
during training helps improve performance.

##  5 Conclusions

In this paper, we address the limitations of the conventional SOTA FIQA method
that use sample relative classifiability as pseudo-labels. This approach often
assign inaccurate pseudo-labels to images with low intra-class variation,
regardless of their actual quality. The proposed novel method is simple yet
very effective in identifying classes that are at risk of being mislabeled
during training and excluding them from the training process, incurring
negligible computational cost. Our method does not require a pre-processing
for data cleaning or a pre-trained model and can be trained in an end-to-end
manner. Additionally, by introducing a pipeline that can safely apply data
augmentation in sample relative classifiability method, our proposed approach
outperforms existing methods across various benchmarks, thereby establishing a
new SOTA in the field of FIQA.

## References

  * [1] Best-Rowden, L., Jain, A.K.: Learning face image quality from human assessments. IEEE Transactions on Information forensics and security 13(12), 3064–3077 (2018)
  * [2] Bosse, S., Maniry, D., Müller, K.R., Wiegand, T., Samek, W.: Deep neural networks for no-reference and full-reference image quality assessment. IEEE Transactions on image processing 27(1), 206–219 (2017)
  * [3] Boutros, F., Damer, N., Kirchbuchner, F., Kuijper, A.: Elasticface: Elastic margin loss for deep face recognition. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 1578–1587 (2022)
  * [4] Boutros, F., Fang, M., Klemt, M., Fu, B., Damer, N.: Cr-fiqa: face image quality assessment by learning sample relative classifiability. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5836–5845 (2023)
  * [5] Cao, J., Li, Y., Zhang, Z.: Celeb-500k: A large training dataset for face recognition. In: 2018 25th IEEE International Conference on Image Processing (ICIP). pp. 2406–2410. IEEE (2018)
  * [6] Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: Vggface2: A dataset for recognising faces across pose and age. In: 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018). pp. 67–74. IEEE (2018)
  * [7] Deng, J., Guo, J., Liu, T., Gong, M., Zafeiriou, S.: Sub-center arcface: Boosting face recognition by large-scale noisy web faces. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16. pp. 741–757. Springer (2020)
  * [8] Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: Additive angular margin loss for deep face recognition. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4690–4699 (2019)
  * [9] Deng, J., Zhou, Y., Zafeiriou, S.: Marginal loss for deep face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp. 60–68 (2017)
  * [10] Deng, S., Xiong, Y., Wang, M., Xia, W., Soatto, S.: Harnessing unrecognizable faces for improving face recognition. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 3424–3433 (2023)
  * [11] Grother, P., Tabassi, E.: Performance of biometric quality measures. IEEE transactions on pattern analysis and machine intelligence 29(4), 531–543 (2007)
  * [12] Grother, P.J., Grother, P.J., Ngan, M., Hanaoka, K.: Face recognition vendor test (FRVT). US Department of Commerce, National Institute of Standards and Technology (2014)
  * [13] Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14. pp. 87–102. Springer (2016)
  * [14] He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of tricks for image classification with convolutional neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 558–567 (2019)
  * [15] Hernandez-Ortega, J., Galbally, J., Fierrez, J., Haraksim, R., Beslay, L.: Faceqnet: Quality assessment for face recognition based on deep learning. In: 2019 International Conference on Biometrics (ICB). pp. 1–8. IEEE (2019)
  * [16] Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. In: Workshop on faces in’Real-Life’Images: detection, alignment, and recognition (2008)
  * [17] Huang, Y., Wang, Y., Tai, Y., Liu, X., Shen, P., Li, S., Li, J., Huang, F.: Curricularface: adaptive curriculum learning loss for deep face recognition. In: proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5901–5910 (2020)
  * [18] Kim, M., Jain, A.K., Liu, X.: Adaface: Quality adaptive margin for face recognition. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 18750–18759 (2022)
  * [19] Knoche, M., Hormann, S., Rigoll, G.: Cross-quality lfw: A database for analyzing cross-resolution image face recognition in unconstrained environments. In: 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021). pp. 1–5. IEEE (2021)
  * [20] Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: Sphereface: Deep hypersphere embedding for face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 212–220 (2017)
  * [21] Liu, X., Van De Weijer, J., Bagdanov, A.D.: Rankiqa: Learning from rankings for no-reference image quality assessment. In: Proceedings of the IEEE international conference on computer vision. pp. 1040–1049 (2017)
  * [22] Maze, B., Adams, J., Duncan, J.A., Kalka, N., Miller, T., Otto, C., Jain, A.K., Niggel, W.T., Anderson, J., Cheney, J., et al.: Iarpa janus benchmark-c: Face dataset and protocol. In: 2018 international conference on biometrics (ICB). pp. 158–165. IEEE (2018)
  * [23] Meng, Q., Zhao, S., Huang, Z., Zhou, F.: Magface: A universal representation for face recognition and quality assessment. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 14225–14234 (2021)
  * [24] Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Transactions on image processing 21(12), 4695–4708 (2012)
  * [25] Mittal, A., Soundararajan, R., Bovik, A.C.: Making a “completely blind” image quality analyzer. IEEE Signal processing letters 20(3), 209–212 (2012)
  * [26] Ou, F.Z., Chen, X., Zhang, R., Huang, Y., Li, S., Li, J., Li, Y., Cao, L., Wang, Y.G.: Sdd-fiqa: unsupervised face image quality assessment with similarity distribution distance. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 7670–7679 (2021)
  * [27] Parkhi, O., Vedaldi, A., Zisserman, A.: Deep face recognition. In: BMVC 2015-Proceedings of the British Machine Vision Conference 2015. British Machine Vision Association (2015)
  * [28] Pilarczyk, R., Skarbek, W.: On intra-class variance for deep learning of classifiers. Foundations of Computing and Decision Sciences 44(3), 285–301 (2019)
  * [29] Sengupta, S., Chen, J.C., Castillo, C., Patel, V.M., Chellappa, R., Jacobs, D.W.: Frontal to profile face verification in the wild. In: 2016 IEEE winter conference on applications of computer vision (WACV). pp. 1–9. IEEE (2016)
  * [30] Shi, Y., Jain, A.K.: Probabilistic face embeddings. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6902–6911 (2019)
  * [31] Terhorst, P., Kolf, J.N., Damer, N., Kirchbuchner, F., Kuijper, A.: Ser-fiq: Unsupervised estimation of face image quality based on stochastic embedding robustness. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5651–5660 (2020)
  * [32] Wang, F., Chen, L., Li, C., Huang, S., Chen, Y., Qian, C., Loy, C.C.: The devil of face recognition is in the noise. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 765–780 (2018)
  * [33] Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., Liu, W.: Cosface: Large margin cosine loss for deep face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5265–5274 (2018)
  * [34] Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13(4), 600–612 (2004)
  * [35] Whitelam, C., Taborsky, E., Blanton, A., Maze, B., Adams, J., Miller, T., Kalka, N., Jain, A.K., Duncan, J.A., Allen, K., et al.: Iarpa janus benchmark-b face dataset. In: proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp. 90–98 (2017)
  * [36] Xie, W., Byrne, J., Zisserman, A.: Inducing predictive uncertainty estimation for face recognition. arXiv preprint arXiv:2009.00603 (2020)
  * [37] Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. arXiv preprint arXiv:1411.7923 (2014)
  * [38] Zhang, L., Zhang, L., Mou, X., Zhang, D.: Fsim: A feature similarity index for image quality assessment. IEEE transactions on Image Processing 20(8), 2378–2386 (2011)
  * [39] Zheng, T., Deng, W.: Cross-pose lfw: A database for studying cross-pose face recognition in unconstrained environments. Beijing University of Posts and Telecommunications, Tech. Rep 5(7) (2018)
  * [40] Zhu, Z., Huang, G., Deng, J., Ye, Y., Huang, J., Chen, X., Zhu, J., Yang, T., Lu, J., Du, D., et al.: Webface260m: A benchmark unveiling the power of million-scale deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10492–10502 (2021)