Deep Neural NEtwork

sensors
Article
Deep-Emotion: Facial Expression Recognition Using
Attentional Convolutional Network
Shervin Minaee 1,*, Mehdi Minaei 2 and Amirali Abdolrashidi 3

Citation: Minaee, S.; Minaei, M.;
Abdolrashidi, A. Deep-Emotion:
Facial Expression Recognition Using
Attentional Convolutional Network.
Sensors 2021, 21, 3046. https://
doi.org/10.3390/s21093046
Academic Editor: Mariano Alcañiz
Raya
Received: 15 February 2021
Accepted: 23 April 2021
Published: 27 April 2021
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1 Snapchat Inc., Santa Monica, CA 90405, USA
2 CS Department, Sama Technical College, Azad University, Tonekabon 46817, Iran;
mehdiminaei1376@gmail.com
3 CS Department, University of California, Riverside, CA 92521, USA; amirali.abdolrashidi@email.ucr.edu
* Correspondence: sminaee@snapchat.com
Abstract: Facial expression recognition has been an active area of research over the past few decades,
and it is still challenging due to the high intra-class variation. Traditional approaches for this
problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on
a database of images or videos. Most of these works perform reasonably well on datasets of images
captured in a controlled condition but fail to perform as well on more challenging datasets with more
image variation and partial faces. In recent years, several works proposed an end-to-end framework
for facial expression recognition using deep learning models. Despite the better performance of
these works, there are still much room for improvement. In this work, we propose a deep learning
approach based on attentional convolutional network that is able to focus on important parts of
the face and achieves significant improvement over previous models on multiple datasets, including
FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important
facial regions to detect different emotions based on the classifier’s output. Through experimental
results, we show that different emotions are sensitive to different parts of the face.
Keywords: convolutional neural network; attention mechanism; spatial transformer network;
facial expression recognition
1. Introduction
Emotions are an inevitable part of interpersonal communication. They can be ex-
pressed in many different forms, which may or may not be observed with the naked eye.
Therefore, with the right tools, any indications preceding or following them can be subject
to detection and recognition. There has been an increase in the need to detect a person’s
emotions in the past few years and increasing interest in human emotion recognition
in various fields including, but not limited to, human–computer interfaces [1], anima-
tion [2], medicine [3,4], security [5,6], diagnostics for Autism Spectrum Disorders (ASD)
in children [7], and urban sound perception [8].
Emotion recognition can be performed using different features, such as facial expres-
sions [2,9,10], speech [5,11], EEG [12], and even text [13]. Among these features, facial
expressions are one of the most popular, if not the most popular, due to a number of reasons;
they are visible, they contain many useful features for emotion recognition, and it is easier
to collect a large dataset of faces (than other means for human recognition) [2,14,15].
Recently, with the use of deep learning and especially convolutional neural networks
(CNNs) [16], many features can be extracted and learned for a decent facial expression
recognition system [17,18]. It is, however, noteworthy that, in the case of facial expressions,
many of the clues come from a few areas of the face, e.g., the mouth and eyes, whereas
other parts, such as the ears and hair, play little parts in the output [19]. This means that,
ideally, the machine learning framework should focus only on important parts of the face
and should be less sensitive to other facial regions.
Sensors 2021, 21, 3046. https://doi.org/10.3390/s21093046 https://www.mdpi.com/journal/sensors

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In this work, we propose a deep learning-based framework for facial expression recog-
nition, which takes the above observation into account and uses an attention mechanism
to focus on the salient part of the face. We show that, by using attentional convolutional
network, even a network with a few layers (less than 10 layers) is able to achieve a very
high accuracy rate. More specifically, this paper presents the following contributions:
• We propose an approach based on an attentional convolutional network, which can
focus on feature-rich areas of the face yet remarkably outperforms recent works
in accuracy.
• In addition, we use the visualization technique proposed in [20] to highlight the facial
image’s most salient regions, i.e., the parts of the image that have the strongest impact
on the classifier’s outcome. Samples of salient regions for different emotions are
shown in Figure 1.
Happiness
Sadness
Anger
Anger
Figure 1. The detected salient regions for different facial expressions using our model. The images
in the first and third rows are taken from the FER dataset, and the images in the second and fourth
rows belong to the extended Cohn-Kanade dataset.
In the following sections, we first provide an overview of related works in Section 2.
The proposed framework and model architecture are explained in Section 3. We then
provide the experimental results, overview of databases used in this work, and model
visualization in Section 4. Finally, we conclude the paper in Section 5.
2. Related Works
In one of the most iconic works in emotion recognition by Paul Ekman [21], happiness,
sadness, anger, surprise, fear, and disgust were identified as the six principal emotions
(besides neutral). Ekman later developed FACS [22] using this concept, thus setting
the standard for work on emotion recognition ever since. Neutral was also included later
on in most human recognition datasets, resulting in seven basic emotions. Image samples
of these emotions from three datasets are displayed in Figure 2.

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Figure 2. (Left to right) The six cardinal emotions (happiness, sadness, anger, fear, disgust, and
surprise) and neutral. The images in the first, second, and the third rows belong to the FER, JAFFE,
and FERG datasets, respectively.
Earlier works on emotion recognition relied on the traditional two-step machine
learning approach, where in the first step, some features are extracted from the images
and, in the second step, a classifier (such as SVM, neural network, or random forest) is
used to detect the emotions. Some of the popular hand-crafted features used for facial
expression recognition include the histogram of oriented gradients (HOG) [23,24], local
binary patterns (LBP) [25], Gabor wavelets [26], and Haar features [27]. A classifier then
assigns the best emotion to the image. These approaches seemed to work fine on simpler
datasets, but with the advent of more challenging datasets (which have more intra-class
variation), they started to show their limitations. To obtain a better sense of some of
the possible challenges with the images, we refer the readers to the images in the first row
of Figure 2, where the image shows only a partial face or the face is occluded with a hand
or eyeglasses.
With the great success of deep learning and more specifically convolutional neural
networks for image classification and other vision problems [28–35], several groups devel-
oped deep learning-based models for facial expression recognition (FER). To name some
of the promising works, Khorrami in [17] showed that CNNs can achieve a high accuracy
in emotion recognition and used a zero-bias CNN on the extended Cohn–Kanade dataset
(CK+) and the Toronto Face Dataset (TFD) to achieve state-of-the-art results. Aneja et al. [2]
developed a model of facial expressions for stylized animated characters based on deep
learning by training a network to model the expression of human faces, one for that of
animated faces, and one to map human images into animated ones. Mollahosseini [9]
proposed a neural network for FER using two convolution layers, one max pooling layer,
and four “inception” layers, i.e., sub-networks. Liu in [10] combined feature extraction and
classification in a single looped network, citing the two parts’ needs for feedback from each
other. They used their Boosted Deep Belief Network (BDBN) on CK+ and JAFFE, achieving
state-of-the-art accuracy.
Barsoum et al. [36] used a deep CNN on noisy labels acquired via crowd-sourcing for
ground truth images. They used 10 taggers to relabel each image in the dataset and used
various cost functions for their DCNN, achieving decent accuracy. Han et al. [37] proposed
an incremental boosting CNN (IB-CNN) in order to improve the recognition of spontaneous
facial expressions by boosting discriminative neurons, which showed improvements over
the best methods at the time. Meng in [38] proposed an identity-aware CNN (IA-CNN) that
used identity- and expression-sensitive contrastive losses to reduce the variations in learn-
ing identity- and expression-related information. In [39], Fernandez et al. proposed an
end-to-end network architecture for facial expression recognition with an attention model.
In [40], Want et al. proposed a simple yet efficient Self-Cure Network (SCN) that
suppresses uncertainties efficiently and prevents deep networks from overfitting uncertain
facial images (due to noisy labels). Specifically, SCN suppresses the uncertainty from two

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different aspects: (1) a self-attention mechanism over a mini-batch to weight each training
sample with a ranking regularization and (2) a careful relabeling mechanism to modify
the labels of these samples in the lowest-ranked group. In [41], Wang et al. developed
a facial expression recognition algorithm that is robust to real-world pose and occlusion
variations. They proposed a novel Region Attention Network (RAN) to adaptively capture
the importance of facial regions for occlusion and pose variant FER. Some of the other
recent works on facial expression recognition includes Multiple attention network for facial
expression recognition [42], deep self-attention network for facial emotion recognition [43],
and a recent survey on facial expression recognition [44].
All of the above works achieved significant improvements over traditional works on
emotion recognition, but they are missing a simple method for recognizing important facial
regions for emotion detection. In this work, we try to address this problem by proposing
a framework based on an attentional convolutional network that is able to focus on salient
facial regions.
3. The Proposed Framework
We propose an end-to-end deep learning framework based on an attentional convolu-
tional network to classify the underlying emotion in facial images. Often times, improving
a deep neural network relies on adding more layers/neurons, facilitating gradient flow
in the network (e.g., by adding skip layers), or better regularizations (e.g., spectral normal-
ization), especially for classification problems with a large number of classes. However,
for facial expression recognition, due to the small number of classes, we show that using
a convolutional network with less than 10 layers and attention (which is trained from
scratch) is able to achieve promising results, presenting better results than state-of-the-art
models for several databases.
Given a facial image, it is clear that not all parts of the face are important for detecting
a specific emotion, and in many cases, we only need to pay attention to specific regions to
get a sense of the underlying emotion. Based on this observation, we added an attention
mechanism, through spatial transformer network into our framework to focus on important
facial regions.
Figure 3 illustrates the proposed model architecture. The feature extraction part
consists of four convolutional layers, with every two followed by a max-pooling layer and
a rectified linear unit (ReLU) activation function. They were then followed by a dropout
layer and two fully connected layers. The spatial transformer (the localization network)
consisted of two convolution layers (each followed by max-pooling and ReLU) and two
fully connected layers. After regressing the transformation parameters, the input was
transformed to the sampling grid T(θ), producing the warped data. The spatial transformer
module essentially tries to focus on the most relevant parts of the image by estimating
a sample over the region of interest. One can use different transformations to warp the input
to the output; here, we used an affine transformation, which is commonly used for many
applications. For further details about the spatial transformer network, please refer to [45].
Convolution
3x3x10
kernel
Max
Pooling
ReLU
FC
(810-to-50)
FC
(50-to-7)
Softmax
Localization
Network
Convolution
3x3x8
kernel
Max
Pooling
ReLU
Convolution
3x3x10
kernel
Max
Pooling
ReLU
ReLU
Convolution
3x3x10
kernel
Convolution
3x3x10
kernel
Max
Pooling
ReLU
ReLU
Convolution
3x3x10
kernel
Dropout
FC
(90-to-32)
ReLU
Linear
θ τθ(G)
Grid
generator
Figure 3. The proposed model architecture.

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This model was then trained by optimizing a loss function using the stochastic gradient
descent approach (more specifically, the Adam optimizer). The loss function in this work
is simply the summation of two terms, the classification loss (cross-entropy), and the reg-
ularization term (which is the `2 norm of the weights in the last two fully-connected
layers).
Loverall = Lclassi f ier + λkw(f c)k2
2 (1)
The regularization weight, λ, is tuned based on the model performance on the valida-
tion set to pick the corresponding value that yields the highest performance on the valida-
tion set. Adding both dropout and `2 regularization enables us to train our models from
scratch even on very small datasets, such as JAFFE and CK+. It is worth mentioning that
we trained a separate model for each of the databases used in this work. We also tried
using a network with similar architecture but more than 50 layers, but the accuracy did not
improve significantly. Therefore, we decided to use the network with fewer layers, which
has a much faster inference speed and is more suitable for real-time applications.
4. Experimental Results
In this section, we provide the detailed experimental analysis of our model on several
facial expression recognition databases. We first provide a brief overview of the databases
used in this work, then provide the performance of our models on four databases, and
compare the results with some of the promising recent works. We then provide the salient
regions detected by our trained model using a visualization technique.
4.1. Databases
In this work, we provide the experimental analysis of the proposed model on sev-
eral popular facial expression recognition datasets, including FER2013 [14], the extended
Cohn–Kanade [46], Japanese Female Facial Expression (JAFFE) [15], and Facial Expres-
sion Research Group Database (FERG) [2]. Before diving into the results, we give a brief
overview of these databases.
FER2013: The Facial Expression Recognition 2013 (FER2013) database was first intro-
duced in the ICML 2013 Challenges in Representation Learning [14]. This dataset contains
35,887 images of 48 × 48 resolution, most of which are taken in wild settings. Originally,
the training set contained 28,709 images, and the validation and test sets include 3589 im-
ages each. This database was created using the Google image search API, and faces were
automatically registered. The faces are labeled as any of the six cardinal expressions as well
as neutral. Compared to the other datasets, FER has more variation in the images, including
facial occlusion (mostly with a hand), partial faces, low-contrast images, and eyeglasses.
Four sample images from the FER dataset are shown in Figure 4.
Figure 4. Four sample images from the FER database.
CK+: The extended Cohn–Kanade (known as CK+) facial expression database [46]
is a public dataset for action unit and emotion recognition. It includes both posed and
non-posed (spontaneous) expressions. The CK+ comprises a total of 593 sequences across
123 subjects. In most previous work, the last frame of these sequences is taken and used for
image-based facial expression recognition. Six sample images from this dataset are shown
in Figure 5.

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Figure 5. Six sample images from the CK+ database.
JAFFE: This dataset contains 213 images of the 7 facial expressions posed by 10 Japanese
female models. Each image has been rated on the six emotional adjectives by 60 Japanese
subjects [15]. Four sample images from this dataset are shown in Figure 6.
Figure 6. Four sample images from the JAFFE database.
FERG: FERG is a database of stylized characters with annotated facial expressions.
The database contains 55,767 annotated face images of six stylized characters. The char-
acters were modeled using MAYA. The images for each character are grouped into seven
types of expressions [2]. Six sample images from this database are shown in Figure 7.
We mainly wanted to try our algorithm on this database to see how it performs on cartoon-
ish characters.
Figure 7. Six sample images from the FERG database.

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4.2. Experimental Analysis and Comparison
We now present the performance of the proposed model on the above datasets. In each
case, we trained the model on a subset of that dataset, validated it on a validation set,
and reported the accuracy over the test set.
Before getting into the details of the model’s performance on different datasets,
we briefly discuss our training procedure. We trained one model per dataset in our exper-
iments, but we tried to keep the architecture and hyperparameters similar among these
different models. Each model was trained for 300 epochs from scratch, on an AWS EC2
instance with a Nvidia Tesla K80 GPU. We initialized the network weights with random
Gaussian variables with zero mean and 0.05 standard deviation. For optimization, we
used an Adam optimizer with a learning rate of 0.005 (different optimizers were used,
including stochastic gradient descents, and Adam seemed perform slightly better). We
also added L2 regularization with a weight decay value of 0.001. It took around 2–4 h
to train our models on the FER and FERG datasets. For JAFFE and CK+, since there are
much fewer images, it took less than 10 min to train a model. For datasets with large class
imbalance, we used oversampling on the classes with fewer samples to force the different
classes to be of the same order. Data augmentation was used for the images in the training
sets to train the model on a larger number of images and to train model for invariances on
small transformations. We used flipping, small rotation, and small distortion to augment
the data.
As discussed before, the FER-2013 dataset is more challenging than the other facial
expression recognition datasets we used. Besides the intra-class variation of FER, another
main challenge in this dataset is the imbalanced nature of different emotional classes. Some
of the classes such as happiness and neutral have many more examples than others. We
used all 28,709 images in the training set to train the model, validated on 3500 validation
images, and reported the model accuracy on the 3589 images in the test set. We were able
to achieve an accuracy rate of around 70.02% on the test set.
The comparison of the result of our model with some of the previous works on FER
2013 is provided in Table 1.
Table 1. Classification accuracies on the FER 2013 dataset.
Method Accuracy Rate
Unsupervised Domain Adaptation [47] 65.3%
Bag of Words [48] 67.4%
VGG+SVM [49] 66.31%
GoogleNet [50] 65.2%
FER on SoC [51] 66%
Mollahosseini et al. [9] 66.4%
The proposed algorithm 70.02%
Aff-Wild2 (VGG backbone) [52] 75%
For the FERG dataset, we used around 34,000 images for training, 14,000 for validation,
and 7000 for testing. For each facial expression, we randomly select 1000 images for testing.
We were able to achieve an accuracy rate of around 99.3%. The comparison between
the proposed algorithm and some of the previous works on FERG dataset are provided
in Table 2.

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Table 2. Classification accuracy on the FERG dataset.
DeepExpr [2] 89.02%
Ensemble Multi-feature [53] 97%
Adversarial NN [54] 98.2%
For the JAFFE dataset, we used 120 images for training, 23 images for validation,
and 70 images for test (10 images per emotion in the test set). The overall accuracy on this
dataset is around 92.8%. The comparison with previous works on the JAFFE dataset is shown
in Table 3.
Table 3. Classification accuracy on the JAFFE dataset.
LBP+ORB features [55] 88.5%
Fisherface [56] 89.2%
Deep Features + HOG [57] 90.58%
Salient Facial Patch [58] 91.8%
CNN+SVM [59] 95.31%
For CK+, 70% of the images were used as training, 10% was used for validation, and 20%
was used for testing (which corresponds to 420, 60, and 113 images for the training, validation,
and test sets, respectively). The comparison of our model with previous works on the extended
CK dataset is shown in Table 4.
Table 4. Classification accuracy on CK+.
MSR [60] 91.4%
3DCNN-DAP [61] 92.4%
LBP+ORB features [55] 93.2%
Inception [9] 93.2%
Deep Features + HOG [57] 94.17%
IB-CNN [37] 95.1%
IACNN [38] 95.37%
DTAGN [62] 97.2%
ST-RNN [63] 97.2%
PPDN [64] 97.3%
Dynamic cascaded classifier [65] 97.8%
4.3. Confusion Matrix
The confusion matrix of the proposed model on the test set of FER dataset is shown
in Figure 8. As we can see, the model makes more mistakes for classes with less samples
such as disgust and fear.

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Figure 8. The confusion matrix of the proposed model on the test set of the FER dataset. The number
of images for each emotion class in the test set is as follows: angry: 328, disgust: 25, fear: 99,
happiness: 930, neutral: 1290, sadness: 450, and surprise: 450.
The confusion matrix of the proposed model on the test set of the FERG dataset is
shown in Figure 9.
Figure 9. The confusion matrix on the FERG dataset. Each emotion class has 1000 images in the
test set.
The confusion matrix of the proposed model on the JAFFE dataset is shown in Figure 10.

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Figure 10. The confusion matrix on the JAFFE dataset. There are a total of seven emotions, and each
class has 10 images in the test set.
4.4. Model Visualization
Here, we provide a simple approach for visualizing important regions while classi-
fying different facial expressions, inspired by the work in [20]. We start from the top-left
corner of an image, and each time, we zero out a square region of size N × N inside the im-
age and make a prediction using the trained model on the occluded image. If occluding
that region makes the model provide a wrong prediction in terms of facial expression
label that region is considered a potential region of importance for classifying that specific
expression. On the other hand, if removing that region does not impact the model’s predic-
tion, we infer that region as being not very important in detecting the corresponding facial
expression. Now, if we repeat this procedure for different sliding windows of N × N, each
time shifting them with a stride of s, we can obtain a saliency map for the most important
regions in detecting an emotion from different images.
We show nine example cluttered images for a happy and an angry image from the
JAFFE dataset and how zeroing out different regions would impact the model prediction
in Figure 11. As we can see, for a happy face, zeroing out the areas around the mouth
would cause the model to make a wrong prediction, whereas for an angry face, zeroing out
the areas around eye and eyebrow makes the model make a mistake.
Figure 12 shows the important regions of seven sample images from the JAFFE dataset,
each corresponding to a different emotion. There are some interesting observations from
these results. For example, for the sample image with neutral emotion in the fourth row,
the saliency region essentially covers the entire face, which means that all of these regions
are important to infer that a given image has a neutral facial expression.

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Figure 11. The impact of zeroing out different image parts on the model prediction for a happy face (the top three rows)
and an angry face (the bottom three rows).
This makes sense, since changes in any part of the face (such as the eyes, lips, eyebrows,
and forehead) could lead to a different facial expression, and the algorithm needs to analyze
all of those parts in order to correctly classify a neutral image. This is however not the case
for most of the other emotions, such as happiness, and fear, where the areas around
the mouth turns out to be more important than other regions.

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Happy
Sad
Angry
Neutral
Surprised
Disgust
Fear
Figure 12. The important regions for detecting different facial expressions. As it can be seen,
the saliency maps for happiness, fear, and surprise are sparser than that for the other emotions.
It is worth mentioning that different images with the same facial expression could
have different saliency maps due to the different gestures and variations in the image.
In Figure 13, we show the important regions for three images with a facial expression
of “fear”. As seen in this figure, the important regions for these images are very similar
when detecting the mouth, but the last one also considers some parts of forehead as impor-
tant regions. This could be because of the strong presence of forehead lines, which is not
visible in the two other images.

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Figure 13. The important regions for three images of fear.
4.5. Model Convergence
In this part, we present the model classification accuracy on the validation set during
the training. This helps us obtain a better understanding of the model convergence speed.
The model performance on the validation set for the FERG dataset is presented in Figure 14.
As seen, the general trend in validation accuracy increases over time but there are some
oscillations at some epochs.
Figure 14. The validation accuracy over different epochs for the model trained on the FERG dataset.

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These oscillations could be avoided by choosing a smaller learning rate, but that
could lead to slower convergence while training the model. It is worth mentioning that,
in the end, the model with the highest validation accuracy is used to report the test error.
Through experimental results, we noticed that the choice of learning rate is very important
and that choosing a learning rate larger than 0.01 usually leads to model divergence. This
could be because of the limited number of samples for some of the datasets.
5. Conclusions
This paper proposes a new framework for facial expression recognition using an
attentional convolutional network. We believe attention to special regions is important for
detecting facial expressions, which can enable neural networks with less than 10 layers
to compete with (and even outperform) much deeper networks in emotion recognition.
We also provide an extensive experimental analysis of our work on four popular facial
expression recognition databases and show some promising results. Additionally, we
deployed a visualization method to highlight the salient regions of face images that are
the most crucial parts thereof for detecting different facial expressions.
Author Contributions: S.M. contributed to the algorithm design and also experimental studies, and
writing some part of the paper. M.M. contirbuted to the verification of experimental studies, and also
revising and re-writing some part of the paper. A.A. contributed to some part of experimental study,
and also writing some part of the paper. All authors have read and agreed to the published version
of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: We express our gratitude to the people at University of Washington Graphics
and Imaging Lab (GRAIL) for providing us with access to the FERG database. We also thank
our colleagues and partners for reviewing our work and for providing very useful comments
and suggestions.
Conflicts of Interest: The authors declare no conflict of interests.
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