Human Eye Localization And Face Recognition Based On Binary Edge Map

Automatic Face Recognition (AFR) is a technology for person authentication by using the digitized facial features. For the past two decades, it has become one of the most challenging research topics in the field of image processing, pattern recognition and computer vision. Because of its tremendous potential applications in law enforcement, security control, and video surveillance, AFR has attracted more and more attentions from many research institutes and government organizations including Departments in charge of defense, security, and information.A Fully Automatic Face Recognition System (FAFRS) consists of functions including face detection from an input image, face image pre-processing, facial feature extraction, and similarity measurement of two face images. These problems have been intensively investigated by researchers and many useful algorithms have been developed. Since faces of different subjects are often similar while face images from the same person often differ quite significantly due to pose and expression variations, and also because the quality of face images is affected greatly by lighting conditions, current face recognition systems cannot meet the requirements of many practical applications.This thesis focuses on the research of human eye localization and the development of face recognition algorithms with good robustness to illumination variations. The positions of two eyes are commonly used for the geometry normalization of a face image, eye locating is thus a very crucial step for the establishment of an FAFRS. The second research focus of this thesis was proposed because an FAFRS with good illumination robustness will be more applicable. Based on the binary edge image obtained using our proposed method, a novel eye localization method is presented in this thesis. Also based on the Binary Edge Map (BEM) obtained using the LAT (Locally Adaptive Threshold) algorithm, a new face recognition algorithm with better illumination robustness is presented. In particular, this thesis makes three main contributions detailed below.Firstly, a new face edge extraction method based on the multi-resolution property of Wavelet Transform (WT) is proposed. The method is composed of an image reconstruction step with high frequency components, two adaptive binarization steps and a noise removing step. The quantitative and qualitative evaluation show that face components, such as eyebrows, eyes, nose and mouth in the resulting Binary Edge Image (BEI) are all extracted with clear details and without touching neighboring face components in most cases. Furthermore, BEI is of good robustness to lighting changes. All these suggest that the BEI is suitable for face component segmentation and the extraction of some key feature points in the face image.Secondly, a novel method for the localization of human eyes is presented. The method consists of three steps, that is, face region extraction, eye region extraction, and finely locating of eyes. In order to improve the correct rate of eye locating, an algorithm for the refinement of face boundaries and a multi-level eye detection scheme are included in this method. In addition, thereflected light dots in the iris are used as an important cue for eye localization. Accordingly, an algorithm for the automatic detection of reflected light spots is given. Experimental results on a set of 150 Bern images and another set of 564 AR images show that correct eye locating rates of 98.7% and 96.6% have been achieved, respectively. The proposed eye detection method is also robust to the variations in views, lighting conditions and facial expressions. Furthermore, it is observed that by using reflected light dots for eye localization, those eyes partially covered by hair can be correctly located.Finally, a face recognition method based on binary template matching is proposed. This approach uses BEM's for face representation. The similarity measure of face images is expressed as the ratio of the number of foreground pixels in the corresponding region to the total number of foreground pixe...