This Spotlight explains how to build an automated system for face, emotion, and pain recognition. The steps involved include pre-processing, face detection and segmentation, feature extraction, and finally recognition to classify features and show the accuracy of the system. State-of-the-art algorithms are used to describe all possible solutions of each step. For face detection and segmentation, several approaches are described to detect a face in images: Viola-Jones, color-based approaches, histogram-based approaches, and morphological operation. Local binary patterns, edge detectors, wavelets, discrete cosine transformation, Gabor filters, and fuzzified features are used for feature extraction. The last step includes three approaches for recognition: classification techniques (with a special focus on deep learning), statistical modeling, and distance/similarity measures.
Automatic facial analysis is a fundamental subject in affective computing. Its main applications involve human-computer interaction. The systems developed for this purpose consider combinations of different modalities, based on vocal and visual cues. This Spotlight takes the foregoing modalities into account to review the conventional and state-of-the-art automatic facial expression analysis systems and their applications. In addition, this ebook surveys the available public databases in this field. We hope to provide readers with a good starting point to start their research in the field of human behavior analysis and facial expression analyses.