# Domain-specific Learning of Multi-scale Facial Dynamics for Apparent
Personality Traits Prediction

Fang Li1  1School of Communication and Information Engineering
Shanghai Technical Institute of Electronics and Information, Shanghai, China
Email<EMAIL_ADDRESS>
###### Abstract

Human personality decides various aspects of their daily life and working
behaviors. Since personality traits are relatively stable over time and unique
for each subject, previous approaches frequently infer personality from a
single frame or short-term behaviors. Moreover, most of them failed to
specifically extract person-specific and unique cues for personality
recognition. In this paper, we propose a novel video-based automatic
personality traits recognition approach which consists of: (1) a domain-
specific facial behavior modelling module that extracts personality-related
multi-scale short-term human facial behavior features; (2) a long-term
behavior modelling module that summarizes all short-term features of a video
as a long-term/video-level personality representation and (3) a multi-task
personality traits prediction module that models underlying relationship among
all traits and jointly predict them based on the video-level personality
representation. We conducted the experiments on ChaLearn First Impression
dataset, and our approach achieved comparable results to the state-of-the-art.
Importantly, we show that all three proposed modules brought important
benefits for personality recognition.

##  I Introduction

Personality is defined as behavioral and emotional characteristics that
partially defines a person’s identity and distinguishes the person from others
[1]. Accurately recognizing human personality can benefit various real-world
applications, such as employment [2, 3], mental health diagnosis [4, 5],
shopping preference [6, 7] as well as understanding human cognition and
emotional processes [8, 9]. In the past decades, psychologists have proposed
many traits-based personality models to evaluate some specific aspects of
human personality, which are stable over time for the each person but differ
across individuals [10]. Among these traits-based models, the ’Five-Factor’
model (also called the ’Big-Five’ model) [11] has been widely used in recent
years, which reflects five aspects of human personality, i.e., Extraversion
(EX), Agreeableness (AG), Openness (OP), Conscientiousness (CO), and
Neuroticism (NE).

However, the majority of traditional personality traits assessments are based
on an individual’s own verbal report (e.g., questionnaires/inventories [12]),
which is subjective and not reliable as the subject may provide incorrect
answers to hide some crucial information on some occasions (e.g., interviews).
Thus, objective solutions that can accurately reflect human personality from
their non-verbal behaviors is a potential alteration. Considering that many
psychological studies suggested that personality traits can be reflected by
human facial behaviors [13, 14, 15], face video-based automatic personality
analysis has drawn a lot of attention in recent years.

A popular and standard video-based solution predicts personality traits from
individuals’ appearance or short-term non-verbal visual behaviors. These
methods [16, 17, 18, 19, 20, 21] individually feed each pre-processed visual
image or short segment of the video into machine learning (ML) models to
predict the corresponding subject’s personality traits. In other words, such
methods usually use the personality annotations that represent the personality
traits reflected by the whole video as the label for each image/short segment,
and attempt to train ML models that can predict personality from an image or a
short segment. This is problematic because a single image/short segment of
different videos (with different personality labels) may carry similar facial
behavior information, resulting in that same input patterns paired with
different labels during the training, which may not allow the ML model to
learn a good hypothesis [22, 23]. Instead, this training strategy may lead the
ML model focus on learning the identity information rather than facial
behaviors as the identity is an invariant attribute of the video while short-
term facial behaviors usually vary a lot in a video. Besides, all these
methods failed to use the long-term behaviors of subjects, which may contain
crucial clues for personality recognition. In this paper, we define the long-
term behaviors as the video-level behaviors.

While personality traits is defined as human attributes that stable over time,
some studies attempted to model long-term visual information for personality
recognition. Instead of inferring personality using features only extracted
from a single frame/short video segment, these methods proposed to construct a
video-level descriptor to represent long-term facial behavior information.
While a simple solution [24, 3] to achieve this is to compute statistics of
frame-level descriptors of the entire video, many recent deep learning-based
approaches [25, 26, 27, 28] proposed to deep learn personality-related video-
level descriptors from the pre-selected key frames of the video. However,
during the key frames selection, these deep learning methods discard a large
number of frames, i.e., crucial personality-related behavior details may be
discarded. To encode all frames of video into a video-level representation,
Song et al. [29, 9] and Shao et al. [30] proposed to use person-specific CNN
weights as the personality descriptor. However, the main drawback of such
methods is that they need self-supervised learn a person-specific CNN or a set
of person-specific layers for each test video and thus are not efficient and
not appropriate for the fast personality assessment.

Although long-term facial behaviors are more reliable sources for personality
recognition, short-term behaviors may also contain informative clues (this is
evidenced by that some short-term automatic modeling methods [18, 19] also
achieved good performance in recognizing apparent personality traits).
Motivated by this, this paper propose to deep learn personality-related clues
from both short-term and long-term facial behaviors for automatic apparent
personality prediction. Firstly, we propose a C3D-Transformer network as the
backbone to extract multi-scale spatio-temporal personality-related facial
clues from every short segments of the video, where domain-specific learning
[31] is extended to allow the proposed C3D-Transformer only learning
personality-related features from the input. Since the lengths of videos are
usually arbitrary in the real-world applications, we utilize the spectral
algorithm [22, 23] to encode all short-term descriptors as a long-term video-
level facial behavior representation, which again encode multi-scale video-
level behavioral temporal information. Finally, we train a personality traits
prediction model that jointly takes each short-term descriptor as well as its
corresponding video-level descriptor as the input, where domain-specific
learning is again employed to further remove the noises from the input
features. The pipeline of the proposed approach is depicted in Fig. 1. The
novelty and contributions of the paper are listed as follows:

  * •

We propose a novel C3D-Transformer network that can deep learn personality-
related multi-scale short-term spatio-temporal facial clues. To the best of
our knowledge, this is very first work that combines C3D and transformer for
learning spatial-temporal personality-related facial behavior features. The
experimental results shows that the proposed C3D-Transformer outperforms the
C3D/ResNet networks that has been frequently used for short-term personality
visual feature extraction.

  * •

We propose a novel domain-specific learning strategy to specifically remove
the personality unrelated noises (especially the identity information) from
both short-term and long-term facial behavior features.

  * •

We empirically evaluate the proposed approach on the ChaLearn dataset and
achieved comparable performance to the state-of-the-art.

Figure 1: The full pipeline of the proposed personality recognition approach.

##  II Related Work

###  II-A Psychological relationship between personality and facial behaviors

A large number of studies show that humans are capable of interpreting
personality from previous non-seen static face [1-7] or spatio-temporal facial
behaviors. For example, the symmetry of the face signifies the extraversion of
a person [5]. The indicator of facial width to height ratio (fWHR), is shown
with correlations of various personality traits, such as achievement striving
[6], dominance [7], aggressiveness [8-11], and risk-taking [12]. The stability
of the FWHR indicator, regardless of the hair, means that static facial traits
can convey enough personality characteristics [13]. Meanwhile, other facial
attributes, such as wrinkles or smile lines, are also shown to be informative
for personalities. Since these facial muscle formations are developed by
personality relevant habitual emotional facial expressions [3]. The facial
expression of smile is often used as cue to judge a person’s extraversion,
neuroticism, and agreeableness while cheerful facial expressions are often
perceived to influence the level of extraversion in Big Five. In [19],
researchers found that gaze behaviors (either in direction of gaze, blink
rates or eye contact) can reflect the personality characteristics, and
preference of direct gaze avoidance and subjective averted gaze, are often
perceived with higher neuroticism scores. In addition, eople with more eye
contact, vocal variety, gesticulation, and facial expressions, are often
interpreted with high score of extraversions [22]. In terms of temporal
behaviors, people with fixated longer duration of fearful eye-region faces,
are perceived with high score of neuroticisms, compared to those with shorter
duration of that [21].

###  II-B Video-based automatic personality analysis approaches

In this section, we explicitly review the existing video-based automatic
apparent personality traits recognition methods. As discussed in Sec. I, these
methods can be roughly categorized into two types based on their temporal
encoding strategies: methods that predict personality from a frame or a short
video segment and methods that predict personality from the whole video.

For the frame/short segment-level methods, they usually predict personality
traits from each frame or short segment of the video and then fuse all
frame/segment-level predictions as the personality prediction of the video
(the subject). In [18], each video is represented by about 100 images, each of
which is then processed by their DAN (Descriptor Aggregation Network) model,
individually, to generate frame-level predictions. Then, they average all
frame-level predictions to make the video-level personality prediction.
Ventura et al. [16] added a CAM module to the DAN network for visualizing the
most important face part during personality recognition. Similar to [18], this
method also extracts frame-level features and then averages the frame-level
predictions as the video-level personality prediction. In [17], a random frame
in a video is selected as the video representation, which is then fed into a
Deep Residual Network. Celiktutan et al. [19] proposed to extract several low-
level hand-crafted features from each frame, which are then combined to infer
personality traits. Besides, some other methods proposed to extract image-
level facial landmarks [32] or human mid-level attributes [33] to understand
the human personality traits. However, all the aforementioned methods only
attempted to infer personality from either a single frame or a thin slice
(less than 1 second), which are not reliable as these would cause a classic
machine learning issue, the same input pattern has multiple labels, resulting
in poor generalization capability. In addition, a single frame or a short
video segment may be too short to carry enough and reliable information for
recognizing an individual’s personality.

Therefore, many recent studies have been devoted to infer personality from
long-term information, i.e., the whole video. Aran et al. [34] extract hand-
crafted per-frame/segment features from a one-minute video for each person,
such as visual activity features, motion template-based features, and visual
focus of attention features, to predict personality traits. Fang et al. [24]
use normalized hand-crafted interactive features (e.g., intra-personal
features, dyadic features, and one-vs-all features) to represent the video-
level personality information, which are processed by Support Vector Machine
(SVM) and Ridge Regression for personality recognition. In [3], a mean vector
of Pyramid multi-level Binarized Statistical Image Features (PML-BSIF) and a
PML Local phase Quantization (PML-LPQ) method are proposed to summarize all
frames’ information of a video. Both video-level representations are then fed
to a machine learning system to evaluate job candidates’ personality traits.

Besides, the deep learning-based approach attracted more attention in recent
years. One of the most popular solutions for learning video-level personality
descriptor is to downsample a whole video into a set of representative frames
and then summarize these frames or their features into a unified
representation. For example, in [25], each video is down-sampled to 32 frames
for both global scene and local face region, respectively. Then, these frames
are jointly fed to a ResNet-based framework to estimate the five traits at the
video level. Zhang et al. [27] also select a face frame from each thin slice,
and then concatenate all selected frames as the video-level representation. In
this work, they employed a consensus strategy to jointly process all selected
frames for video-level personality prediction. Instead of discarding a large
number of frames as [25, 27], Song et al. [29] proposed a domain adaptation
method that encodes all frames information of a video into a set of
intermediate CNN layers that contains person-specific dynamics of the
corresponding subject. Then, they use the weights and biases of the learned
intermediate CNN layers as the person-specific representation for personality
recognition. Additionally, they also propose to search a person-specific CNN
architecture for each individual, representing the cognitive process of the
target individual [9, 30]. Similarly, each person-specific CNN architecture is
then parametrized as a video-level graph representation which is fed to Graph
Neural Networks for personality recognition.

##  III The proposed approach

In this section, we present the proposed approach that deep learns both multi-
scale short-term and long-term personality-related facial dynamics for
automatic personality traits recognition. Specifically, we first propose a
C3D-Transformer network as the backbone to extract multi-scale short-term
facial behaviors for each frame of the given video (Sec. III-A1), where the
domain-specific learning is proposed here to enforce the well-trained network
to only learn personality-related information rather than noises such as
identities (Sec. III-A2). To encode a video-level representation, we further
propose to summarize all deep-learned short-term facial features of the given
video into a spectral representation that encodes multi-scale facial temporal
information (Sec. III-B). Finally, we feed the produced spectral
representation into an ANN model for personality traits prediction, where the
domain-specific learning is again employed to remove the personality-unrelated
information from the input video-level spectral representation (Sec. III-C).

In comparison to previous approaches, the main advantages of our method are:
1) Unlike [16, 17, 18, 19, 20] failed to consider multi-scale facial temporal
information, our method proposes C3D-Transformer network to model multi-scale
short-term temporal dependencies between frames, which is crucial for
representing facial behaviors; 2) we present the first work that specifically
separates the learned short-term facial behaviour features and video-level
representations into two parts: personality-specific features and unrelated
noises; 3) Different from [25, 27] that remove a large number of frames, we
propose to encode all short-term facial behavior features extracted from all
available frames into a video-level representation which retains multi-scale
video-level facial dynamics.

###  III-A Short-term facial dynamics modelling

####  III-A1 C3D-Transformer network architecture

While a facial image only contain a static facial display and the
corresponding person’s identity, the short-term facial behavior carried by an
image sequence can provide more discriminative spatio-temporal behavioral
information for personality recognition. While some previous methods [35]
employed short-term facial behaviors for personality recognition, they used
RNNs/LSTMs to model facial dynamics from low-dimensional latent features,
which may ignore crucial dynamic information during the feature extraction
process.

To avoid the aforementioned problems, this paper proposes a C3D-transformer
network to model multi-scale short-term facial dynamics from the original
video segments. As shown in Fig. 1, the C3D-transformer consists of a 3D
convolution block which ensure the facial behavioral information within
multiple frames can be directly extracted without using LSTM/RNNS. To be
specific, the employed C3D block consists of three convolution blocks, where
each contains a 3D convolution layer, a 3D batch normalization layer and a
ReLU activation function to simulate non-linear projections. For a video
segment, the output of the C3D block is a set of 3D feature maps, which are
then aggregated as a single multi-channel time-series feature map. Let us
assume that the produced 3D feature maps of size $C\times T\times W\times H$,
where $C$ is the number of feature maps while $T$, $W$, $H$ are the temporal
scale, width and height of each feature map. The size of the aggregated multi-
channel time-series feature map is $CWH\times T$, i.e., the time series has
$CWH$ channels and $T$ frames. After that, the transformer module firstly
down-samples the produced multi-channel time-series feature map along the
temporal dimension. Consequently, multiple multi-channel time-series feature
maps with temporal scales of $T_{1},T_{2},\cdots,T_{K}$ can be produced, each
of which are then fed to a fully connected layer to generate a 1D
representation, respectively. As a result, these 1D representations contain
multi-scale temporal information of the input video segment. Finally, multiple
multi-head transformers are introduced to individually process each of these
1D representations, and the outputs of all transformers are concatenated as
the segment-level representation. In comparison to previous approaches which
failed to consider multi-scale facial temporal information, the proposed
C3D-Transformer can model multi-scale short-term temporal dependencies between
frames, which is crucial for representing facial behaviors;

####  III-A2 Domain-specific training strategy

Although the proposed C3D-transformer can already deep learn personality-
related features from the input data, as discussed in Sec. I, pairing video-
level labels with short segments may lead the network focus on learning some
personality-unrelated noises, which are invariant attributes of the person in
the video, e.g., identity, and ignoring their spatio-temporal behaviors. To
solve this problem, this section proposes a domain-specific learning strategy
to remove such noises.

As shown in Fig. 2, during the training, the representations produced by all
transformers are individually fed to five pairs of encoders, where each pair
contains a personality encoder that corresponds to a specific personality
trait and a noise encoder to extract noises that un-relate to the personality
traits. In other words, the assumption is that the extracted feature at each
temporal scale (from each transformer) is made up of two parts: personality
trait-related feature and un-related noises. In particular, all encoders
consist of two fully connected layers with a ReLU and a dropout attached. To
train them as well as the C3D-Transformer, several loss functions are
introduced. Firstly, we feed each output of the personality encoder to a
classifier in order to jointly predict the corresponding five personality
traits, which is supervised by the MSE loss:

$\text{Loss}_{1}=\sum_{i=1}^{5}\left({pd}_{i}-label_{i}\right)^{2}$ (1)

where ${pd}_{i}$ is the $i_{th}$ personality traits prediction and $label_{i}$
is the corresponding label of the video segment. This loss would enforce all
personality encoders to specifically learn personality-related information
from the output of the C3D-Transformer. Meanwhile, each personality feature
extracted from the personality encoder should be dissimilar to the
corresponding unrelated noises, which can be denoted as:

$\text{Loss}_{2}=\sum_{i=1}^{5}\left\|({fea}^{P-Enc(i)})^{\top}{fea}^{N-Enc(i)}\right\|_{\text{Frob}}^{2}$
(2)

where ${fea}^{P-Enc(i)}$ is the personality feature extracted from the
$i_{th}$ personality encoder, and ${fea}^{N-Enc(i)}$ is the noise extracted
from the noise encoder. $\|\cdot\|^{2}_{F}$ is the square Frobenius norm.

Figure 2: Illustration of the proposed domain-specific learning (DS) module
for each trait. It should be noted that our full pipeline consists of five DS
modules, where each module learns a representation for predicting a individual
trait.

To further ensure that the personality-related features and un-related noises
are separated by each pair of encoders, we additionally introduce five
decoders. Each encoder also consists of two fully connected layers, and each
fully connected layer is followed by a ReLU and a dropout layer. A decoder
takes features generated from the corresponding personality and noise
encoders, targeting to reconstruct the original temporal feature generated by
the corresponding transformer. This process can be formulated as:

$\text{Loss}_{3}=\frac{\sum_{d=1}^{D}\left(fea_{i}^{d}(\text{Dec})-fea_{i}^{d}(\text{Tran})\right)^{2}}{I\times
D}$ (3)

where $fea_{i}^{d}(\text{Dec})$ and $fea_{i}^{d}(\text{Tran})$ are the
$k_{th}$ element of the reconstructed feature corresponding to $i_{th}$ trait
and the $k_{th}$ element of the $i_{th}$ transformer’s output. In comparison
to [31], we disregard the similarity loss that forces personality-related
features generated by each input samples to be the same. This is because we do
not want that the input samples at each batch are required to have the same
personality traits (which is impossible). As a result, the final loss function
to train the C3D-Transformer and the personality-related encoders is defined
as:

$\text{Loss}_{\text{overall}}=\alpha\text{Loss}_{1}+\beta\text{Loss}_{2}+\gamma\text{Loss}_{3}$
(4)

where $\alpha$, $\beta$ and $\gamma$ are corresponding weights for each loss.

###  III-B Long-term facial dynamics modelling

After obtaining frame-level or short video segment-level predictions, most
previous studies compute the video-level prediction by averaging all frame-
level predictions or combining short segment-level predictions. However, such
simple strategies failed to consider the long-term behavioral temporal
information. In this paper, we first employs the spectral heatmaps proposed by
[23, 22] to re-summarize multi-scale behavioral temporal information from all
segment-level features of the given video, to construct a multi-scale video-
level behavioral representation.

Supposing that for an given video with $N$ short segments, the proposed short-
term modelling module extracts $N$ features with $D$ dimensions, the video-
level spectral heatmaps is constructed as follows.

  * •

Step 1: Arranging all segment-level features as a multi-channel time-series
signal with $D$ channels and $N$ time stamps.

  * •

Step 2: Converting each channel of time-series as a spectral signal with $N$
dimensions using Discrete Fourier Transform, resulting in a $D\times N$
amplitude map and a $D\times N$ phase map.

  * •

Step 3: As discussed in [22], most behavior information are retained by low
frequency components. Thus, we then select only top-M lowest frequencies from
both amplitude map and phase map. Consequently, a $D\times M$ amplitude map
and a $D\times M$ phase map can be generated. Since both amplitude and phase
maps are symmetric, the $M<\frac{N}{2}$ is set here.

In short, the produced amplitude and phase maps contain $M$ video-level
frequencies, where the values of each frequency components indicate behavioral
information of a unique temporal scale. This is to say, these two spectral
maps are video-level representations that encode $M$ scales behavioral
dynamics of the given video. In this paper, we concatenate two the amplitude
and phase maps as a two-channel heatmap.

###  III-C Multi-task personality traits prediction module

In this section, we present our personality traits prediction model for
jointly predicting five personality traits. As shown in Fig. 3, the model
consists of two modules: single-trait module and multi-traits module. The
single-trait module contains five branches, each of which contains three 1D
convolution blocks (a 1D convolution layer, a dropout layer and a ReLU) to
generate the trait-specific feature from the input heatmaps independently.
Then, the multi-traits module that contains two 1D residual convolution
blocks, a global average pooling layer and three fully connected layers are
proposed to combine five sets of trait-specific feature maps and model the
underlying relationship among them, in order to further enhance the prediction
performance.

Figure 3: Multi-task personality traits prediction model.

During the training, each branch of the single-trait module is connected with
two fully connected layers and aims to output predictions of the corresponding
trait. This would provide intermediate supervision for each branch, enforcing
each of them focusing on learning features that only relates to the target
personality trait, which is denoted as:

$\text{Loss}_{\text{single}}=(pd_{i}^{single}-label_{i})^{2}$ (5)

Meanwhile, we also attach another Mean Square Error loss function to evaluate
the five traits’ predictions generated by the multi-trait module. This loss is
defined as

$\text{Loss}_{\text{multi}}=\sum_{i=1}^{5}(pd_{i}^{multi}-label_{i})^{2}$ (6)

As a result, the utilized loss functions can not only allow each single-trait
branch to focus on modelling the trait-related information but also the multi-
trait module to further learn the relationship among these extracted trait-
specific features, providing more valuable clues for personality traits
prediction.

##  IV Experiments

In this section, we first list and explain the database used for evaluation in
Sec. IV-A. Then, the evaluation metrics are detailed in Sec. IV-B and we
present all model settings and training details in Sec. IV-C. After that, the
experimental results including comparison to other methods as well as ablation
studies are listed and discussed in Sec. IV-D.

###  IV-A Database

Apparent personality estimation experiments were conducted on the ChaLearn
[36] database. This database records $10,000$ talking-to-the-camera videos
from $2,764$ YouTube users, where each video is $15$ seconds with fps of $30$.
It has been divided to three subsets: a training set (contains $6,000$
videos), a validation set (contains $2,000$ videos) and a test set (contains
$2,000$ videos). Each video is labeled with six dimensionas e.g., Big-Five
personality traits and the interview’ dimension, using Amazon Mechanical Turk
by several human annotators, and the ground-truth of each dimension ranges
from 0 to 1.

###  IV-B Metrics

To compare our approach with other methods on the ChaLearn dataset, the
widely-used mean accuracy measurement ACC [36] is employed:

$\mbox{ACC}=1-\frac{1}{N}\sum_{i=1}^{N}|p_{i}-g_{i}|,$ (7)

where $g_{i}$ and $p_{i}$ are the labels and predictions, respectively and $N$
is the number of videos.

###  IV-C Implementation details

Pre-processing: To obtain the face region from each frame, we used OpenFace
2.0 [37] to process all frames of each video, and used the aligned face images
as the input to our C3D-Transformer network.

Model settings: In this paper, the multi-scale encoding module contains two
down-sampling rates, i.e., choosing a frame for every $2$ frames and $5$
frames. As a result, we used three transformers that take the original feature
map and two down-sampled feature maps, respectively. Each transformer network
consists of two self-attention layers and four fully connected layers with six
heads, where the last fully connected layer output a $64$ dimension vector as
the segment-level personality representation. We set the spectral heatmaps
that are used for encoding long-term to keep top-32 selected frequencies for
each channel, result in a $64\times 32$ amplitude map and a $64\times 32$
phase map for each video. The probability of dropout layers in the personality
prediction model are all set to $0.5$.

Training details: We first train the C3D network by pairing the video-level
label with the each short segment using MSE loss function and Adam optimizer
with learning rate of $0.005$, and then re-train the entire C3D-Transformer in
an end-to-end manner, where we re-used Adam optimizer with learning rate of
$0.001$. The batch size for training C3D and C3D-Transformer are both set to
$3$, and at each batch, there are $30$ frames. Then, the personality
prediction model (the MLP) is trained using SGD optimizer with learning rate
of $0.0005$ and batch size is set to $64$ spectral heatmaps. In this paper, we
used the pre-defined $6000$ videos for training, $2000$ videos for validation
and $2000$ videos for testing. All experiments and code were implemented based
on PyTorch.

###  IV-D Results

In this section, we first compare our best system with the existing approaches
in Sec. IV-D1, showing the competitive performance of the proposed approach.
Then, we report the results achieved by a set of ablation studies in Sec.
IV-D2, which provide the explicit evaluation for the influence of the proposed
C3D-Transformer, domain-specific learning, video-level spectral encoding and
multi-task learning modules on the recognition performance.

####  IV-D1 Comparison with existing methods

We compare our best system which consist of three modules: C3D-Transformer
learned with the domain-specific training strategy, spectral-based video-level
feature encoding, and the multi-task regressor, to other recently proposed
approaches in Table I. In addition, we also report the results achieved by the
same models trained without domain-specific strategy. It is clear that our
best system achieved comparable average result to state-of-the-arts methods,
i.e., our average performance (0.9163) is very close to the best approach
(0.917) [27] and the second best approach (0.9168) [29]. More specifically,
our approach has promising performances in recognizing the Agreeableness and
Openness traits, which achieved the best recognition result for Openness trait
and the second best result for Agreeableness trait. In addition, the proposed
approach also beat most listed approaches in recognizing Conscientiousness and
Neuroticism traits.

These results indicate that the proposed framework can provide relative
reliable predictions for most personality traits (Our approach is the state-
of-the-art face-based system to recognize the Openness trait) only from
individuals’ non-verbal facial behaviors. Although two approaches [27, 29]
achieved slightly better average performance than our approach. One of them
requires extra the use of emotion data and annotations [27] while the other
suffers from very long person-specific layer training duration [29]. In
contrast, the proposed approach neither requires extra data and annotation nor
needs long training time. In other words, the proposed approach not only can
provide similar recognition performance as [27, 29] but also much easier to be
implemented.

Methods | Extra | Agree | Consc | Neuro | Open | Avg.
---|---|---|---|---|---|---
Baseline [38] | 0.9019 | 0.9059 | 0.9073 | 0.8997 | 0.9045 | 0.9039
PML [26] | 0.9155 | 0.9103 | 0.9137 | 0.9082 | 0.9100 | 0.9115
NJU-LAMDA [18] ∗ | 0.9112 | 0.9135 | 0.9128 | 0.9098 | 0.9105 | 0.9116
DCC [17] ∗ | 0.9088 | 0.9097 | 0.9109 | 0.9085 | 0.9092 | 0.9109
PAL [29] | 0.9183 | 0.9262 | 0.9082 | 0.9133 | 0.9180 | 0.9168
PerEmoN [27] | 0.920 | 0.914 | [0.921] | [0.914] | 0.915 | [0.917]
Ours (Non-DS) | 0.9135 | 0.9116 | 0.9082 | 0.9107 | 0.9159 | 0.9120
Ours (DS) | 0.9138 | [0.9190] | 0.9166 | 0.9123 | 0.9198 | 0.9163

TABLE I: ACC results achieved by the proposed approaches and existing
approaches on ChaLearn First Impression Dataset, where the bold numbers denote
the best result and bracketed numbers denote the second best result. Non-DS
denotes the C3D-Transformer-spectral-multitask-regressor model trained without
domain-specific learning strategy while DS denotes the same model trained with
domain-specific learning strategy.

####  IV-D2 Ablation studies

Methods | Extra | Agree | Consc | Neuro | Open | Avg.
---|---|---|---|---|---|---
C3D [39] | 0.8892 | 0.8918 | 0.8893 | 0.8919 | 0.8915 | 0.8907
TPN [40] | 0.8935 | 0.8929 | 0.8855 | 0.8991 | 0.8918 | 0.8926
C3D-Transformer | 0.9052 | 0.9129 | 0.9025 | 0.9098 | 0.9070 | 0.9075

TABLE II: ACC results achieved by C3D, TPN and the proposed C3D-Transformer.

We first compare the proposed C3D-Transformer with standard C3D and a standard
multi-scale temporal CNNs (Temporal Pyramid Network (TPN) [40]) in Table. II
and Fig. 4(a). All three systems are trained by standard MSE loss function,
and the input of the TPN is set as the same as the C3D-Transformer while we
feed $15$ frames for C3D network for each trail. For all systems, the video-
level prediction were obtained by averaging the segment-level predictions. It
is clear that adding the proposed multi-scale temporal transformer can largely
enhance the performance of the C3D network over all five traits, with $1.9\%$
average ACC improvement. Meanwhile, it also outperformed the TPN network for
all five traits, with $1.7\%$ average ACC improvement. These results not only
demonstrate that personality traits-related clues are contained in multi-scale
facial dynamics but also show that the attention operations in transformer can
better combine multi-scale cues than standard convolution operations. In
short, the proposed C3D-Transformer has strong ability to capture short-term
personality-related facial clues.

Figure 4: The results of ablation studies.

We then evaluate the impact of the proposed domain-specific learning strategy.
As illustrated in Fig. 4(b), the ACC results achieved by C3D-Transformer
trained with domain-specific learning allows significant improvement for four
traits (Extraversion, Conscientiousness, Neuroticism, and Openness), while the
achieved similar results as the C3D-Transformer trained with standard MSE for
the Extraversion trait. Meanwhile, we also found that the C3D trained with the
proposed domain-specific learning strategy outperformed the C3D trained with
standard MSE in predicting all five traits. We believe these results show the
fact that the domain-specific learning strategy can efficiently remove the
noises or personality un-related information from the learned multi-scale
dynamic features, and therefore achieved enhanced recognition performances for
both C3D and C3D-Transformer models.

As discussed in Sec. I, the hypothesis of this paper is that long-term facial
clues are more important for personality traits recognition. Thus, we also
compare the results achieved by the C3D-Transformer with and without video-
level modelling step. In particular, we extend the spectral encoding to
summarize both segment-level features and predictions to generate the video-
level prediction. As shown in Fig. 4(c), the spectral-based video-level
modelling improved the average performance for both C3D-Transformers that are
trained with MSE or domain-specific learning strategy. This is because the
spectral encoding captures multiple video-level frequencies of the segment-
level data, which describe multi-scale facial behaviors for personality traits
prediction, which are ignored when simply averaging segment-level predictions.
In short, we conclude that a further long-term modelling of the short-term
features can improve the video-level personality recognition performance. A
similar conclusion also has been made for another task [41].

Finally, we evaluate the importance of the multi-task regression by comparing
the proposed multi-task learning scheme to the systems that individually
predicts each trait, i.e., training five regressors, where each takes the
video-level spectral feature for predicting a single trait. According to the
results in Fig. 4(d), for spectral features of the C3D-Transformer trained
with MSE, the proposed multi-task regression generated better performance for
Extraversion, Conscientiousness, and Openness traits. Meanwhile, for spectral
features of the C3D-Transformer trained with domain-specific learning
strategy, the proposed multi-task regression generated better performance for
Extraversion, Agreement, Conscientiousness, and Openness traits as well as the
average result. Despite the multi-task learning did not provide significant
improvements for most conditions, it still enhanced most traits’ recognition
as well as the average performance, which means this scheme allows the
underlying relationship information among five traits, which are personality-
related, to be used for personality traits recognition.

##  V Conclusion

In this paper, we propose a video-based automatic apparent personality
recognition approach which has three main advantages: 1. we propose a novel
multi-scale spatio-temporal transformer to learn multi-scale short-term
behavioural temporal information of the video; 2. the proposed domain-specific
learning strategy can extract the relevant cues from the deep-learned features
for each personality trait’s recognition; 3. our approach can model the
underlying relationship from the video-level representation of the five
traits. The experimental results show that although the proposed system avoids
complex pre-processing as in [25] and high computational complexity as in
[29], it still achieved a top performance among existing approaches.
Specifically, we show that: 1. the proposed C3D-Transformer is a superior
backbone for learning short-term personality-related facial behaviors from
short video segments; 2. the proposed domain-specific learning strategy
clearly improved the systems that are learned by standard training process,
demonstrating our strategy can enhance the network’s ability to learn
depression-related cues; 3. the video-level personality modelling provide more
reliable predictions than these learned from short-term facial behaviors.

The main limitation of this work is that while the proposed approach can be
treated as a general video classification/regression framework, we only
evaluated it on personality computing. Also, the short segment-level modelling
and video-level modelling were conducted separately rather than trained by an
end-to-end scheme. Consequently, our future work would focus on two parts: 1.
extending the proposed approach to more video analysis tasks and provide a
fair evaluation of it; 2. developing an end-to-end training method allowing
our model to jointly model the short-term and long-term facial behaviors for
personality recognition.

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# pynucastro: an interface to nuclear reaction rates and code generator for
reaction network equations

Donald E. Willcox  Department of Physics and Astronomy, Stony Brook University
Michael Zingale  Department of Physics and Astronomy, Stony Brook University

††margin:  DOI: 10.21105/joss.00588 Software • Review • Repository • Archive
Submitted: 15 February 2018
Published: 18 March 2018 Licence
Authors of papers retain copyright and release the work under a Creative
Commons Attribution 4.0 International License (CC-BY).

## Summary

pynucastro addresses two needs in the field of nuclear astrophysics: visual
exploration of nuclear reaction rates or networks and automated code
generation for integrating reaction network ODEs. pynucastro accomplishes this
by interfacing with nuclear reaction rate parameterizations published by the
JINA Reaclib project (Cyburt et al. 2010).

Interactive exploration is enabled by a set of classes that provide methods to
visualize the temperature dependency of a rate, evaluate it at a particular
temperature, and find the exponent, n, for a simple $\rm{T^{n}}$
parameterization. From a collection of rates, the flow between the nuclei can
be visualized interactively using Jupyter widgets. These features help both
with designing a network for a simulation as well as for teaching nuclear
astrophysics in the classroom.

After selecting a set of rates for a given problem, pynucastro can construct a
reaction network from those rates consisting of Python code to calculate the
ODE right hand side. Generated Python right hand sides evolve species in the
reaction network, and pynucastro includes a Python example integrating the CNO
cycle for hydrogen burning.

pynucastro can also generate Fortran code implementing reaction networks,
using SymPy (Meurer et al. 2017) to determine the system of ODEs comprising
the network. From the symbolic expressions for the ODE right hand side,
pynucastro also generates a routine to compute the analytic Jacobian matrix
for implicit integration.

Fortran networks incorporate weak, intermediate, and strong reaction rate
screening for the Reaclib rates (Graboske et al. 1973; Alastuey and Jancovici
1978; Itoh et al. 1979). These networks can also include selected weak
reaction rate tabulations (Suzuki, Toki, and Nomoto 2016). To calculate energy
generation in Fortran networks, pynucastro uses nuclear binding energies from
the Atomic Mass Data Center (Huang et al. 2017; Wang et al. 2017) and the 2014
CODATA recommended values for the fundamental physical constants (Mohr,
Newell, and Taylor 2016).

pynucastro is capable of generating two kinds of Fortran reaction networks.
The first type is a standalone network with a driver program to integrate
species and energy generation using the variable-order ODE integration package
VODE (Brown, Byrne, and Hindmarsh 1989). This Fortran driver program is
designed to be easy to use and can integrate reaction networks significantly
faster than is possible for the generated Python networks.

Secondly, pynucastro can generate a Fortran network consisting of right hand
side and Jacobian modules that evolve species, temperature, and energy
generation for the StarKiller Microphysics code. Via StarKiller Microphysics,
astrophysical simulation codes such as Castro (Almgren et al. 2010) and
Maestro (Nonaka et al. 2010) can directly use pynucastro reaction networks.
pynucastro includes a carbon burning network with tabulated $\rm{A=23}$ Urca
weak reactions currently used for studying white dwarf convection with Maestro
(Zingale et al. 2017).

Future work will focus on implementing nuclear partition functions to compute
reverse reaction rates in the Reaclib library (Rauscher and Thielemann 2000;
Rauscher 2003). It is also in some cases necessary to compute reverse reaction
rates using detailed balance with a consistent nuclear mass model instead of
using the parameterized reverse reaction rates in Reaclib (Lippuner and
Roberts 2017). Additionally, work is ongoing to port the networks generated
for StarKiller Microphysics to CUDA Fortran to support parallel reaction
network integration on GPU systems (Zingale et al. 2017). We intend to
implement this port directly into the pynucastro-generated networks.

We wish to thank Abigail Bishop for discussions about code generation for the
StarKiller Microphysics code as well as for exploratory calculations. We are
grateful to Max P. Katz for numerous discussions that enabled the ongoing port
of pynucastro-generated networks to CUDA Fortran. We also wish to thank
Christopher Malone for discussions about various implementation details in
pynucastro as well as sample code to improve element identification. We
especially thank Josiah Schwab for helpful discussions about nuclear partition
functions and reverse rates as well as for testing pynucastro and pointing out
issues in visualization and documentation. This work was supported by
DOE/Office of Nuclear Physics grant DE-FG02-87ER40317.

## References

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dense stellar matter.” _The Astrophysical Journal_ 226 (December): 1034–40.
doi:10.1086/156681.

Almgren, A. S., V. E. Beckner, J. B. Bell, M. S. Day, L. H. Howell, C. C.
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Huang, W.J., G. Audi, Meng Wang, F.G. Kondev, S. Naimi, and Xing Xu. 2017\.
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Meurer, Aaron, Christopher P. Smith, Mateusz Paprocki, Ondřej Čertík, Sergey
B. Kirpichev, Matthew Rocklin, Amit Kumar, et al. 2017. “SymPy: Symbolic
Computing in Python.” _PeerJ Computer Science_ 3 (January): e103.
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Mohr, Peter J., David B. Newell, and Barry N. Taylor. 2016. “CODATA
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Nonaka, A., A. S. Almgren, J. B. Bell, M. J. Lijewski, C. M. Malone, and M.
Zingale. 2010. “MAESTRO:An Adaptive Low Mach Number Hydrodynamics Algorithm
for Stellar Flows.” _The Astrophysical Journal Supplement Series_ 188: 358–83.
https://doi.org/10.1088/0067-0049/188/2/358.

Rauscher, T. 2003. “Nuclear Partition Functions at Temperatures Exceeding 1010
K.” _The Astrophysical Journal Supplement Series_ 147 (August): 403–8.
doi:10.1086/375733.

Rauscher, T., and F.-K. Thielemann. 2000. “Astrophysical Reaction Rates from
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Suzuki, Toshio, Hiroshi Toki, and Ken’ichi Nomoto. 2016. “Electron-capture and
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Wang, Meng, G. Audi, F.G. Kondev, W.J. Huang, S. Naimi, and Xing Xu. 2017\.
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_Chinese Physics C_ 41 (3): 030003.
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Zingale, M., A. S. Almgren, M. G. Barrios Sazo, V. E. Beckner, J. B. Bell, B.
Friesen, A. M. Jacobs, et al. 2017. “Meeting the Challenges of Modeling
Astrophysical Thermonuclear Explosions: Castro, Maestro, and the AMReX
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