A Study On Robust Face Recognition

It is difficult to implement the face recognition mechanism using computers for several reasons. First, human face is a deformable object composed of complex 3D curve surfaces, which is hard to be represented in form of mathematics. Secondly, faces of different persons have the similar structure. On the other side, the face images are greatly dependent on ages and photography conditions. This results that the difference between two face images of two different persons taken under the same photography condition is probably less than that between two images of the same person under different conditions. Therefore, in order to implement a practical face recognition system, the recognition method needs to be independent of the above variations. This kind of method is named as Robust Face Recognition technology in this study. A Robust Face Recognition system takes use of many technologies among which feature detection is the most important. According to the above understanding, this study targets on Robust Face Recognition based on feature detection, and makes the following creative contributions:1. An adaptive multi-cue based facial feature detection approach is presented. By combining various facial cues through inducing, verifying and error-correcting, this approach enables to detect facial features robustly under varying poses, background and lightning conditions. It increases the correction ratio significantly (up to 10% for pose variation) compared with typical existing methods and makes it possible to detect facial features for practical use.2. A cross-verification mechanism is worked out for detecting facial features on moving pictures by merging the technologies of both image analysis and motion analysis. It provides a solution to the problem of error-correction and feature estimation and makes it possible to detect facial features on video segment at a correction ration of 98%, thus paves the way for the application of content-based retrieval and video encoding.3. The concept of Feature Fidelity is defined based on Principal Component Analysis (PCA) and an adaptive threshold based feature detector is then proposed, which takes no advantage of ad hoc knowledge including threshold, photography condition, experimental coefficients, etc., thus greatly expands the applications of facial feature detection technology.4. An adaptive face detection method is proposed, which combines feature detection and color analysis and makes it possible to detect multiple faces on the complex background under varied lightning conditions.5. A study on face recognition for varied poses and large face database is made on the basis of automatic multi-view modeling and multi-template matching.