Facial asymmetry-based feature extraction for different applications: a review complemented by new advances

Abstract

Facial feature extraction (FFE) is considered as a challenging area for computer vision and artificial intelligence research community. There are numerous application domains of face recognition such as demographic classification and facial disease classification. In last few years, numerous feature extraction approaches are proposed. This review focuses on studies that exclusively use facial asymmetry as one of the main subject-specific facial characteristics for FFE. This paper provides a review about the related research conducted in the past two decades. First, we summarize the conventional FFE approaches and their main algorithms. Application of deep networks to facial asymmetry based FFE approaches is then presented. Multi-network deep models suitable for asymmetry-based FFE for different applications are also focused in this review. We presented the details about the publicly available face datasets, evaluation metrics, and comparison of the state-of-the-art results is also presented. The directions of asymmetry-based FFE for future research is also presented to provide an awareness about the existing and future trends. This review is presented to provide directions and ideas for future research in the field of facial asymmetry.