A discriminatively deep fusion approach with improved conditional GAN (im-cGAN) for facial expression recognition

Facial expression is one form of communication which being non-verbal in nature precedes verbal communication in both origin and conception. Most of the existing methods for Automatic Facial Expression Recognition (AFER) are mainly focused on global feature extraction assuming that all facial regions contribute equal amount of discriminative information to predict the expression class. The detection and localization of facial regions that have significant contribution to expression recognition and extraction of highly discriminative feature distribution from those regions are not fully explored. The key contributions of the proposed work are developing novel feature distribution upon combining the discriminative power of shape and texture feature; determining the contribution of facial regions and identifying the prominent facial regions that hold abstract and highly discriminative information for expression recognition. The shape and texture features taken into consideration are Local Phase Quantization (LPQ), Local Binary Pattern (LBP), and Histogram of Oriented Gradients (HOG). Multiclass Support Vector Machine (MSVM) is used while one versus one classification. The proposed work is implemented on CK+, KDEF, and JAFFE benchmark facial expression datasets. The recognition rate of the proposed work is 94.2% on CK+ and 93.7% on KDEF, which is significantly more than the existing handcrafted feature-based methods. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.12700

Automatic facial expression recognition combining texture and shape features from prominent facial regions

Emotion recognition plays a significant role in cognitive psychology research. However, measuring emotions is a challenging task. Thus, several approaches have been designed for facial expression recognition (FER). Although, the challenges increases further as the data transits from laboratory-controlled environment to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-the-wild</i> circumstances. Nowadays, applications are overwhelmed by a profusion of deep learning (DL) techniques in the real-world problems. DL networks have steadily led to a better understanding of low-dimensional discriminative features from high-dimensional complex face patterns for automatic FER. The modern FER systems based on deep neural networks mainly suffer from two problems: overfitting due to the inadequate availability of training data and complications unassociated with the expressions, such as occlusion, posture, illumination, and identity bias. This study aims to provide a comprehensive survey of the significant DL-based methods that have made a notable contribution to the field of FER. Different components of the methods, such as pre-processing, feature extraction, and classification of facial expressions, are described systematically. Moreover, the discussed approaches are analyzed to compare their performance along with their advantages and limitations. Further, different databases relevant to FER are also explored in this study. Essentially, the main aim of this survey is twofold. The former is to discuss the current scenario of FER approaches and the latter is to present some thoughts on the future directions of facial emotion recognition by machines: what are the obstacles and prospects for FER researchers?. 

Understanding Deep Learning Techniques for Recognition of Human Emotions Using Facial Expressions: A Comprehensive Survey

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Wenmeng Yu

Papers

Co-attentive multi-task convolutional neural network for facial expression recognition