Abstract
In recent years, facial expression analysis and recognition (FER) have emerged as an active research topic with applications in several different areas, including the human-computer interaction domain. Solutions based on 2D models are not entirely satisfactory for real-world applications, as they present some problems of pose variations and illumination related to the nature of the data. Thanks to technological development, 3D facial data, both still images and video sequences, have become increasingly used to improve the accuracy of FER systems. Despite the advance in 3D algorithms, these solutions still have some drawbacks that make pure three-dimensional techniques convenient only for a set of specific applications; a viable solution to overcome such limitations is adopting a multimodal 2D+3D analysis. In this paper, we analyze the limits and strengths of traditional and deep-learning FER techniques, intending to provide the research community an overview of the results obtained looking to the next future. Furthermore, we describe in detail the most used databases to address the problem of facial expressions and emotions, highlighting the results obtained by the various authors. The different techniques used are compared, and some conclusions are drawn concerning the best recognition rates achieved.
Highlights
Introduction to Facial ExpressionRecognition (FER)Facial Expression Recognition is a computer-based technology that uses mathematical algorithms to analyze faces in images or video
The last step analyzes the movement of facial features and classifies them into emotion or attitude categories, taking the name of Facial Emotion Recognition, a topic of emotion recognition that involves the analysis of human facial expressions in multimodal forms
Paul Ekman as universal, and other emotions have been considered, to develop sounder algorithms able to deal with any occlusions
Summary
Facial Expression Recognition is a computer-based technology that uses mathematical algorithms to analyze faces in images or video. The facial analysis is developed in three primary phases: face detection, facial landmark detection, and facial expression and emotion classification. The last step analyzes the movement of facial features and classifies them into emotion or attitude categories, taking the name of Facial Emotion Recognition, a topic of emotion recognition that involves the analysis of human facial expressions in multimodal forms. The acronym FER, in literature, often refers to both facial expression recognition and facial emotion recognition [1]. In this paper, it stands for Facial
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