Face Recognition Based On Gabor Wavelet Transform

Biometric identification systems, which use physical features to check a person's identity, ensure much greater security than password and number systems. Identifying a human individual from his or her face is one of the most non-intrusive modalities in biometrics. The capability of finding and recognizing a face in a random scene is important in everyone's activities. So it's very significant that automate this task. Development in computer technology and artificial intelligence make research on these fields be possible. At the same time, it also spurs the efforts on intelligent interface between human and machine. It is fundamental that computer should know who are in its eyeshot. Identifying a human individual from his or her face is one of the most challenging problems. At first, the face images are obtained by different way or in different condition, so they have substantial difference in quality, geometry, illumination, etc. In addition, it also exits the makeup and face-painting influence. But the most essential reason is that face is a kind of non-rigid object that has highly similarity. Different person's faces have similar shape and structure, and one person's face has different state. In the past decade, many research groups make great efforts on it and a series of successes have made general personal identification appear not only technically feasible but also economically practical. However, no perfect solution can accomplish this task under the non-constraint condition. It is of particular interest in a wide variety of applications. In fact, face recognition technology has two kinds of application: recognition/ identification/ match and verification/ authentification/ surveillance. A typical example about the former one is applications in law enforcement for mug-shot identification. The latter application is also broad, such as verification for personal identification, gateways to limited access areas, authentification for ATM and family security, etc. In brief, one is concluding who is he/she and the other is deciding is he/she somebody. The Gabor transform is one of the most important schemes for time-frequency analysis 。Multi-center and Multi-resolution are two characteristics of human vision system, so these years algorithms based on multi-center and multi-resolution analysis are paid much attention to and are widely studied. The Gabor wavelet is a kind of multi-resolution of images.Using Gabor magnitude,Gabor frequency,and Gabor phase,We can represent the object image well.Gabor wavelet transform is applied in the fields of signal detection, texture analysis and image segmentation and recognition. This paper is a study of the recognition of static human face images. The first part of the paper introduced the background, purpose and current research status of human face recognition, as well as the applications of Gabor wavelet transform in the area of human face recognition, also summarized the main methods of human face recognition. In the second part, the author described detailed process of Gabor analysis theory, one-dimensional and two-dimensional Gabor transforms, and an algorithm of human face recognition based on the two-dimensional transform. The expression of human face image in Gabor features and the steps of elastic matching and human face recognition were also described in detail The experiment of the algorithm used the ORL face database, which contains ten different images of each of 40 distinct subjects taken at different times, varying lighting, facial expressions and head poses. In the experiment, the result was acceptable with only one image of each of the 40 distinct subjects and the results got better with the number of images of each subject increasing. To further test the algorithm, the author tried face images with varying lighting and facial expressions and found that face images with simple expression, even lighting and no glass were suitable for training sets. Some possible future research directions are outlined at the end of the paper.The applications of Gabor transform were limited due to the high complexity involved in the computation of thecomplex-valued transform. In this thesis ,computation of Gabor transform was not reduced, this problems should be taken into account in the future.