Face recognition or identification, face detection and face tracking and face detection are all closely related. The purpose of face detection is to determine the location of the image of human face. Suppose an image there is only one face, the facial feature detection aims to detect the presence and location of features such as eyes, nose (nostrils) (eyebrows) (mouth) (mouth) and other ear. Face recognition or identification is the input image and database image comparison, if the report matches. Face recognition is designed to test the input image in the identity of the individual, but the face tracking is real-time, continuous estimation of the image sequence in the face of the location and possible direction. Facial expression recognition involves identifying human emotional states (happy, sad, disgusted, etc.). Clearly, any solution to the problem in the automatic recognition system, face detection is the first step.
From a face image detection method can be divided into the following four:
(1) Knowledge-based Methods It will face the formation of a typical rule base is encoded on the human face. Usually, by the relationship between facial features face detection.
(2) Feature Invariant Approaches The algorithm is aimed at attitude, perspective or lighting conditions change in the case of the structural features found there, and then use these characteristics determine the face.
(3) Template Matching Methods Storage of several standard face model to describe the whole face and facial features; calculate the input image and the stored relationship between model and for testing.
(4) Appearance-based Methods Template matching method with contrast, focus on learning from the training images to obtain model (or template), and these models for testing.
2 Knowledge-based approach
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Knowledge-based approach is rule-based face detection methods, rules, researchers from a priori knowledge on the human face. Generally easier to make simple rules to describe the facial features and their mutual relations, such as the image appears in a face, usually symmetrical with each other two eyes, a nose and a mouth. Relationships between features can be their relative distance and position to describe. First of all, in the input image extracted facial features, determine the rules-based coding of face candidate regions.
This approach is very difficult problems of human knowledge into well-defined rules. If the rules are detailed (strict), because the rules can not detect all possible failures; If the rules are too general (common), there may be a higher false acceptance rate.In addition, it is difficult to extend this approach to under different pose face detection, because the list all the cases is a very difficult task.
Yang and Huang use of hierarchical knowledge-based face detection method, their system formed by the three rules. At the highest level, by scanning the input image window and application of the rules set for each location to find all the possible face candidate area. Description of the rules usually higher face looks like what, while lower-level rules depend on the details of facial features. Hierarchical multi-resolution images generated by the average and the second sample, shown in Figure 1. Encoding rules are usually established under the resolution in the lower face of the candidate areas, including the central part of the face (Figure 2, shaded lighter), of which four are basically the same gray scale units. In the face around the upper part of the same gray. The center of the face and upper part of the surrounding gray different. The lowest resolution (Lever 1) image used to search human face candidate area and the resolution of the later, more sophisticated to make an further processing. Lever 2 completed in the candidate region face local histogram equalization and edge detection. The continued existence of the candidate areas in Lever 3 with other facial features, such as eyes, mouth and other corresponding rules to be checked. Characteristic of this method is to use from the rough - fine strategy to reduce the required
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