Abstract
Emotion recognition using facial images has been a challenging task in computer vision. Recent advancements in deep learning has helped in achieving better results. Studies have pointed out that multiple facial expressions may present in facial images of a particular type of emotion. Thus, facial images of a category of emotion may have similarity to other categories of facial images, leading towards overlapping of classes in feature space. The problem of class overlapping has been studied primarily in the context of imbalanced classes. Few studies have considered imbalanced facial emotion recognition. However, to the authors’ best knowledge, no study has been found on the effects of overlapped classes on emotion recognition. Motivated by this, in the current study, an affinity-based overlap reduction technique (AFORET) has been proposed to deal with the overlapped class problem in facial emotion recognition. Firstly, a residual variational autoencoder (RVA) model has been used to transform the facial images to a latent vector form. Next, the proposed AFORET method has been applied on these overlapped latent vectors to reduce the overlapping between classes. The proposed method has been validated by training and testing various well known classifiers and comparing their performance in terms of a well known set of performance indicators. In addition, the proposed AFORET method is compared with already existing overlap reduction techniques, such as the OSM, ν-SVM, and NBU methods. Experimental results have shown that the proposed AFORET algorithm, when used with the RVA model, boosts classifier performance to a greater extent in predicting human emotion using facial images.
Highlights
Human emotion identification is a growing area in the field of Cognitive Computing that incorporates facial expression [1], speech [2], and texts [3]
The proposed affinity-based overlap reduction technique (AFORET) coupled with the initial stage of the residual variational autoencoder (RVA) model has been tested by using the popular Affectnet Facial
AFORET has been tested for various degrees of data loss starting from 5% up to
Summary
Human emotion identification is a growing area in the field of Cognitive Computing that incorporates facial expression [1], speech [2], and texts [3]. Recognizing emotions from facial expressions is a trivial task for the human brain, but it associates a higher level of complexity when carried out using machines. The reason for this intricacy is the non-verbal nature of the communication that is enacted through facial cues. Emotion prediction through other forms of data sources such as texts are comparatively easier tasks because of the word-level expressions that can be annotated through hashtags or word dictionaries [7,8,9]
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