Facial Expression Recognition

The facial expression has long been an interest for psychology, since Darwin published The expression of Emotions in Man and Animals (Darwin, C., 1899). Psychologists have studied to reveal the role and mechanism of the facial expression. One of the great discoveries of Darwin is that there exist prototypical facial expressions across multiple cultures on the earth, which provided the theoretical backgrounds for the vision researchers who tried to classify categories of the prototypical facial expressions from images. The representative 6 facial expressions are afraid, happy, sad, surprised, angry, and disgust (Mase, 1991; Yacoob and Davis, 1994). On the other hand, real facial expressions that we frequently meet in daily life consist of lots of distinct signals, which are subtly different. Further research on facial expressions required an object method to describe and measure the distinct activity of facial muscles. The facial action coding system (FACS), proposed by Hager and Ekman (1978), defines 46 distinct action units (AUs), each of which explains the activity of each distinct muscle or muscle group. The development of the objective description method also affected the vision researchers, who tried to detect the emergence of each AU (Tian et. al., 2001).

Face Recognition

In the modern life, the need for personal security and access control is becoming an important issue. Biometrics is the technology which is expected to replace traditional authentication methods that are easily stolen, forgotten and duplicated. Fingerprints, face, iris, and voiceprints are commonly used biometric features. Among these features, face provides a more direct, friendly and convenient identification method and is more acceptable compared with the individual identification methods of other biometrics features. Thus, face recognition is one of the most important parts in biometrics.

Face and Eye Detection

Face detection is the most fundamental step for the research on image-based automated face analysis such as face tracking, face recognition, face authentication, facial expression recognition and facial gesture recognition. When a novel face image is given we must know where the face is located, and how large the scale is to limit our concern to the face patch in the image and normalize the scale and orientation of the face patch. Usually, the face detection results are not stable; the scale of the detected face rectangle can be larger or smaller than that of the real face in the image. Therefore, many researchers use eye detectors to obtain stable normalized face images. Because the eyes have salient patterns in the human face image, they can be located stably and used for face image normalization. The eye detection becomes more important when we want to apply model-based face image analysis approaches.

Facial Gesture Recognition

From facial gestures, we can extract many kinds of messages in human communication: they represent visible speech signals and clarify whether our current focus of attention is important, funny or unpleasant for us. They are direct, naturally preeminent means for humans to communicate their emotions (Russell and Fernandez-Dols, 1997). Automatic analyzers of subtle facial changes, therefore, seem to have a natural place in various vision systems including automated tools for psychological research, lip reading, bimodal speech analysis, affective computing, face and visual-speech synthesis, and perceptual user interfaces.

Introduction

Communication between one human and another is the hallmark of our species. According to neuropsychology, the human face is the primary tool in human communication among all social communication instruments (Perry et. al., 1998). One of the most important pieces of information that the human face carries may be the identity. By recognizing the identity of a person, we can feel comfortable with familiar faces, sometimes uncomfortable with unfamiliar ones (as when a baby cries when a strange face shows up), and recall our memories of conversations with the person, which brings rich backgrounds and context information for smooth conversation.

Face Tracking

When we want to analyze the continuous change of the face in an image sequence, applying face tracking methods is a better choice than applying the face detection methods to each image frame. Usually, the face tracking methods are more efficient than the ordinary face detection methods because they can utilize the trajectory of the face in the previous image frames with an assumption that the shape, texture, or motion of the face change smoothly. There have been many approaches to face tracking. We divide the face tracking methods into several categories according to the cues that are extracted for tracking.

Human Motion Analysis

Human motion analysis (Moeslund et. al., 2006; Wang et. al., 2003) is currently one of the most active research areas in computer vision due both to the number of potential applications and its inherent complexity. This high interest is driven by many applications in many areas such as surveillance, virtual reality, perceptual, control applications or analysis of human behaviors. However, the research area contains a number of difficult, such as ill-posed problem. So, many researchers have investigated these problems. Human motion analysis is generally composed of three major parts: human detection, tracking and the behavior understandings.

Face Modeling

In the field of computer vision, researchers have proposed many techniques for representation and analysis of the varying shape of objects, such as active contour (Kass et. al., 1998) and deformable template (Yuille et. al., 1989). However, the active contour, which consists of a set of points, is too flexible to limit its deformation to a reasonable amount of variations for a specific object and it does not have the ability to specify a specific shape. The deformable template, which consists of a set of parametric curves, is difficult to represent all the shape deformations of an object due to 3D rotation or self-deformations because the deformations are too complex to be explained by the combination of hand crafted simple parametric curves.