LEARNING DIVERSIFIED FEATURE REPRESENTATIONS FOR FACIAL EXPRESSION RECOGNITION IN THE WILD Negar Heidari and Alexandros Iosiﬁdis

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LEARNING DIVERSIFIED FEATURE REPRESENTATIONS

FOR FACIAL EXPRESSION RECOGNITION IN THE WILD

Negar Heidari and Alexandros Iosiﬁdis

Department of Electrical and Computer Engineering, Aarhus University, Denmark

ABSTRACT

Diversity of the features extracted by deep neural networks is im-

portant for enhancing the model generalization ability and accord-

ingly its performance in different learning tasks. Facial expression

recognition in the wild has attracted interest in recent years due to

the challenges existing in this area for extracting discriminative and

informative features from occluded images in real-world scenarios.

In this paper, we propose a mechanism to diversify the features ex-

tracted by CNN layers of state-of-the-art facial expression recog-

nition architectures for enhancing the model capacity in learning

discriminative features. To evaluate the effectiveness of the pro-

posed approach, we incorporate this mechanism in two state-of-the-

art models to (i) diversify local/global features in an attention-based

model and (ii) diversify features extracted by different learners in an

ensemble-based model. Experimental results on three well-known

facial expression recognition in-the-wild datasets, AffectNet, FER+

and RAF-DB, show the effectiveness of our method, achieving state-

of-the-art performance of 89.99% on RAF-DB, 89.34% on FER+

and the competitive accuracy of 60.02% on AffectNet dataset.

Index Terms—Facial Expression Recognition, Feature Repre-

sentation, Feature Diversity, Deep Learning, Ensemble Learning

1. INTRODUCTION

Facial expression as a fundamental natural signal for human social

communication plays an important role in different applications of

artiﬁcial intelligence, such as Human Computer Interaction (HCI),

healthcare, and driver fatigue monitoring. Deep Convolutional Neu-

ral Networks (CNNs) have led to considerable progress in automatic

Facial Expression Recognition (FER) on large-scale datasets in real-

world scenarios. FER methods aim to solve a visual perception prob-

lem by learning feature representations from facial images/videos

to be classiﬁed as an emotional category, i.e. happiness, sadness,

fear, anger, surprise, disgust, neutral, and contempt. In laboratory-

controlled datasets, such as CK+ [1] and JAFFE [2], where the facial

images are in ﬁxed frontal pose without any occlusion, FER meth-

ods have achieved excellent performance. However, these methods

confront challenges for in-the-wild datasets, such as AffectNet [3],

FER+ [4], and RAF-DB [5], where facial images come with illumi-

nation, occlusion and pose variations causing considerable change

in facial appearance. To address that, many recent methods rely on

transfer learning to exploit the feature representations learned for

other visual perception tasks, such as object recognition, with well-

designed networks, like ResNet-18 [6], trained on large datasets, like

This work received funding from the European Union’s Horizon 2020

research and innovation programme under grant agreement No. 871449

(OpenDR). This publication reﬂects the authors’ views only. The European

Commission is not responsible for any use that may be made of the informa-

tion it contains.

VGG-Face [7] and MS-Celeb-1M [8], to be transferred for facial

expression recognition in challenging in-the-wild datasets. How-

ever, considering that many face datasets are small and imbalanced,

these deep neural networks are mostly over-parameterized and tend

to overﬁt on the training data, which can degrade their generalization

ability on unseen data.

Increasing the diversity of features learned by different network

layers/neurons has been recognized as an effective way to improve

model generalization [9]. It is theoretically shown in [10,11] that the

within-layer activation diversity improves the generalization perfor-

mance of neural networks and lowers the effect of overﬁtting. In this

paper, we propose a mechanism for learning diversiﬁed facial feature

representations by encouraging the learner to extract diverse spatial

and channel-wise features. This mechanism can be used in different

CNN architectures to increase the features diversity between layers

or branches, spatial regions, and/or channels of feature maps. We

incorporate our proposed optimization mechanism into two state-of-

the-art models, i.e., the MA-Net [12] and the ESR [13], and conduct

experiments on three well-known in-the-wild datasets, i.e., Affect-

Net, FER+ and RAF-DB. Experimental results demonstrate the ef-

fectiveness of learning diversiﬁed features in improving the accuracy

and generalization of the pretrained state-of-the-art models on new

samples.

The contributions of the paper can be summarized as follows:

• We propose a mechanism for learning diversiﬁed features

in spatial and channel dimensions of CNNs to improve the

model’s accuracy in discriminating facial expressions.

• We evaluate our feature extraction mechanism by incorpo-

rating it into two state-of-the-art models which have differ-

ent properties, i.e., one beneﬁts from a region-based attention

mechanism and transfer learning, and the other one is an efﬁ-

cient ensemble-based architecture. In both cases, our diversi-

ﬁed feature learning mechanisms boost the performance.

• Conducted experiments on three benchmark in-the-wild

datasets, including the large-scale dataset AffectNet, indi-

cate the effectiveness and adaptability of our method, which

can be used in different types of models. Our code is publicly

available at https://github.com/negarhdr/Diversiﬁed-Facial-

Expression-Recognition.

2. RELATED WORKS

Recent studies are focused on addressing the challenges of in-the-

wild facial expression recognition by training models with multi-

pose examples [14], and extracting key facial features based on fa-

cial landmarks and region-based attention mechanisms [15,16,17].

Learning facial features from global and local perspectives simulates

the human brain’s perception mechanism and helps achieving better

performance in visual perception problems. MA-Net [12] is a global

arXiv:2210.09381v2 [cs.CV] 19 Feb 2023

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LEARNINGDIVERSIFIEDFEATUREREPRESENTATIONSFORFACIALEXPRESSIONRECOGNITIONINTHEWILDNegarHeidariandAlexandrosIosidisDepartmentofElectricalandComputerEngineering,AarhusUniversity,DenmarkABSTRACTDiversityofthefeaturesextractedbydeepneuralnetworksisim-portantforenhancingthemodelgeneralizationabilityandacc...

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LEARNING DIVERSIFIED FEATURE REPRESENTATIONS FOR FACIAL EXPRESSION RECOGNITION IN THE WILD Negar Heidari and Alexandros Iosiﬁdis

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