CUHK electronic theses & dissertations collection

Face recognition is an important biological authentication technology. In this thesis, we study face recognition using structural approach, in which structural information of the face is extracted and used for the recognition.The first part of this thesis discusses the methods for the detection of some facial features and their applications in face recognition. Generally, the more features with good accuracy are detected and used for face recognition, the better is the recognition result. We first propose a method to extract the eyebrow contours from the face image by an enhanced K-means clustering algorithm and a revised Snake algorithm. The reliable part of the extracted eyebrow contour is then used as a feature for face recognition. Then we introduce a novel method to estimate the chin contour for face recognition. The method first estimates several possible locations of chin and check points which are used to build a number of curves as chin contour candidates. Based on the chin like edges extracted by a modified Canny edge detector, the curve with the largest degree of likeliness to be the actual chin contour is selected. Finally, the estimated chin contours with high enough likeliness are used as a geometric feature for face recognition. Experimental results show that the proposed algorithms can extract eyebrows and chin contours with good accuracy and the extracted features are effective for improving face recognition rates.The second part of this thesis deals with pose estimation and pose invariant face recognition. Pose estimation is achieved based on the detected structural information of the face. We first propose a method for recognition of a face at any pose from a single frontal view image. The first step of the method is feature detection. In this step, we detect the ear points by a novel algorithm. Then, a set of 3D head models is constructed for each test image based on the geometric features extracted from both the input image and each frontal view image in the gallery. Using this set of potential models, we can obtain a set of potential poses. Based on these potential models and poses, feature templates and geometric features of the input face are then rectified to form the potential frontal views. The last step is the feature comparison and final pose estimation. The major contribution of the proposed algorithm is that it can estimate and compensate both sidespin and seesaw rotations while existing model based algorithms from a single frontal view can only handle sidespin rotation. We also propose a method of pose invariant face recognition from multi-view images. First, the 3D poses of face in 2D images are estimated by using a 3D reference face model in three-layer linear iterative processes. The 3D model is updated to fit a particular person using an iterative algorithm. Then we construct the virtual frontal view face images from the input 2D face images based on the estimated poses and the matched 3D face models. We extract the waveletfaces from these virtual frontal views based on wavelet transform and perform linear discriminant analysis on these waveletfaces. Finally, the nearest feature space classifier is employed for feature comparison. These proposed methods were tested using commonly used face databases. Experimental results show that the proposed face recognition methods are robust and compare favourably with existing methods in terms of recognition rate.Chen Qinran."September 2006."Adviser: Wai Kuen Cham.Source: Dissertation Abstracts International, Volume: 68-03, Section: B, page: 1814.Thesis (Ph.D.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (p. 134-154).Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.Abstracts in English and Chinese.School code: 1307