Biometrics is a rapidly developing technology, which has been broadly applied in forensics such as criminal identification, secured access, and prison security. The biometric technology is basically a pattern recognition system that acknowledges a person by finding out the legitimacy of a specific behavioral or physiological characteristic owned by that person. In this era, face is one of the commonly acceptable biometrics system which is used by humans in their visual interaction and authentication purpose. The challenges in the face recognition system arise from different issues concerned with cosmetic applied faces and of low quality images. In this thesis, we propose two novel techniques for extraction of facial features and recognition of faces when thick cosmetic is applied and of low quality images. In the face recognition technology, the facial marks identification method is one of the unique facial identification tasks using soft biometrics. Also facial marks information can enhance the face matching score to improve the face recognition performance. When faces are applied by thick cosmetics, some of the facial marks are invisible or hidden from their faces. In the literature, to detect the facial marks AAM (Active Appearance Model) and LoG (Laplacian of Gaussian) techniques are used. However, to the best of our knowledge, the methods related to the detection of facial marks are poor in performance especially when thick cosmetic is applied to the faces. A robust method is proposed to detect the facial marks such as tattoos, scars, freckles and moles etc. Initially the active appearance model (AAM) is applied for facial feature detection purpose. In addition to this prior model the Canny edge detector method is also applied to detect the facial mark edges. Finally SURF is used to detect the hidden facial marks which are covered by cosmetic items. It has been shown that the choice of this method gives high accuracy in facial marks detection of the cosmetic applied faces. Besides, another aspect of the face recognition based on low quality images is also studied. Face recognition indeed plays a major rule in the biometrics security environment. To provide secure authentication, a robust methodology for recognizing and authentication of the human face is required. However, there are numbers of difficulties in recognizing the human face and authentication of the person perfectly. The difficulty includes low quality of images due to sparse dark or light disturbances. To overcome such kind of problems, powerful algorithms are required to filter the images and detect the face and facial marks. This technique comprises extensively of detecting the different facial marks from that of low quality images which have salt and pepper noise in them. Initially (AMF) Adaptive Median Filter is applied to filter the images. The filtered images are then extracted to detect the primary facial feature using a powerful algorithm like Active Shape Model (ASM) into Active Appearance Model (AAM). Finally, the features are extracted using feature extractor algorithm Gradient Location Orientation Histogram (GLOH).Experimental results based on the CVL database and CMU PIE database with 1000 images of 1000 subjects and 2000 images of 2000 subjects show that the use of soft biometrics is able to improve face recognition performance. The results also showed that 93 percentage of accuracy is achieved. Second experiment is conducted with an Indian face database with 1000 images and results showed that 95 percentage of accuracy is achieved.
BIOMETRICS SECURITY BASED ON FACE RECOGNITION
