Facial Image Processing in Computer Vision

Facial Image Processing in Computer Vision

Moi Hoon Yap (University of Bradford, UK) and Hassan Ugail (University of Bradford, UK)
Copyright: © 2013 |Pages: 13
DOI: 10.4018/978-1-4666-3994-2.ch056
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Abstract

The application of computer vision in face processing remains an important research field. The aim of this chapter is to provide an up-to-date review of research efforts of computer vision scientist in facial image processing, especially in the areas of entertainment industry, surveillance, and other human computer interaction applications. To be more specific, this chapter reviews and demonstrates the techniques of visible facial analysis, regardless of specific application areas. First, the chapter makes a thorough survey and comparison of face detection techniques. It provides some demonstrations on the effect of computer vision algorithms and colour segmentation on face images. Then, it reviews the facial expression recognition from the psychological aspect (Facial Action Coding System, FACS) and from the computer animation aspect (MPEG-4 Standard). The chapter also discusses two popular existing facial feature detection techniques: Gabor feature based boosted classifiers and Active Appearance Models, and demonstrate the performance on our in-house dataset. Finally, the chapter concludes with the future challenges and future research direction of facial image processing.
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Background

The study in facial expression has been conducted in last century (Darwin, 1872; P. Ekman, 1973), and within the past 12 years considerable progress has been made in automatic analysis of facial expression from digital video input (J.F Cohn, 2007; J.F. Cohn & Kanade, 2007; Fasel & Luettin, 2003; Tian, Cohn, & Kanade, 2005). In early research, Ekman et al (P. Ekman & Friesen, 1976) reported a new method of describing facial movement based on an anatomical analysis of facial action. To capture subtlety of human emotion and paralinguistic communication, automated recognition of fine-grained changes in facial expression is needed (Tian, Kanade, & Cohn, 2001).

Early researches in automatic analysis and recognition of facial actions from input video focused on the relatively tractable problem of posed facial actions acquired under well-controlled conditions. Recent work has progressed to spontaneous facial actions, subtle facial actions, variation in illumination and merging with synthesis faces (i.e. avatar separate identity from facial behaviour) (J.F Cohn, 2007). Computer facial expression analysis systems need to analyze the facial actions regardless of context, culture, gender, and so on. The accomplishments in psychological studies, human movement analysis, face detection, face tracking, and recognition motivate the automatic facial expression analysis (Tian, et al., 2005).

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