Abstract
Facial Expression Recognition (FER) is presently the aspect of cognitive and affective computing with the most attention and popularity, aided by its vast application areas. Several studies have been conducted on FER, and many review works are also available. The existing FER review works only give an account of FER models capable of predicting the basic expressions. None of the works considers intensity estimation of an emotion; neither do they include studies that address data annotation inconsistencies and correlation among labels in their works. This work first introduces some identified FER application areas and provides a discussion on recognised FER challenges. We proceed to provide a comprehensive FER review in three different machine learning problem definitions: Single Label Learning (SLL)- which presents FER as a multiclass problem, Multilabel Learning (MLL)- that resolves the ambiguity nature of FER, and Label Distribution Learning- that recovers the distribution of emotion in FER data annotation. We also include studies on expression intensity estimation from the face. Furthermore, popularly employed FER models are thoroughly and carefully discussed in handcrafted, conventional machine learning and deep learning models. We finally itemise some recognise unresolved issues and also suggest future research areas in the field.
Highlights
F ACIAL Expression Recognition (FER) has gained remarkable attention in computing, which is not limited to Computer Vision (CV) and Human-Computer Interaction (HCI)
Experiment on CK+ as sequence dataset shows that Hidden Markov Model (HMM) provided recognition rate of 98.4% [239], which is a good result, but the deep learning model by [240] termed Expression Intensity Invariant Network (EIINet) showed better result with an accuracy of 99.6%
We have successfully presented a holistic review of FER that covers its possible research trends based on the machine learning approaches
Summary
F ACIAL Expression Recognition (FER) has gained remarkable attention in computing, which is not limited to Computer Vision (CV) and Human-Computer Interaction (HCI). FER’s general architecture comprises three major phases; pre-processing, feature extraction, and classification or recognition These phases carry out their respective tasks sequentially on a particular FER database to establish ground truth for the system to achieve its goal. This work presents FER in three different machine learning problem definitions, which include: Single Label Learning (SLL) (Multi-class problem), Multilabel Learning (MLL): where a FER image contains one or more basic emotions. Another approach is Label Distribution Learning (LDL), which proportionally estimate all the basic emotions present in facial expression image.
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