Research And Design Of Identity Verification System Based On Facial Features

The identity verification technology based on biometric features played an increasingly important role in our society; it has been a key research area in information security. Among the measured biometric features, facial feature identification and verification are gaining popularity and diverse applications for the reason that they are considered to be non-invasive, low cost, and natural biometric technologies.In this thesis, several common identity verification technologies are analyzed and the problems and shortages of the present identity verification system are summarized. Base on this, face features are applied to identity verification technology and a face-based identity verification prototype system is designed and implemented. The function module, frame, and key technologies of the system are researched and analyzed, and the performance of the system is tested. The results show that the system can achieve identity verification task precisely, and robustness to pose and lighting variance.The key technologies in research include the detection and location of face and eyes, the image pre-processing, the extraction of facial features and the design of classifier. In this thesis, AdaBoost learning algorithm is applied to construct the classifier to realize face detection and eye location. The result shows that the algorithm can detect and locate face and eyes timely and precisely. Face image pre-processing method is put forward, which reduces the affect of pose and lighting variance on the system.That whether the method of feature extraction is good or not can directly affects the verification ability of the system. Due to the better spatial and orientation selection of the Gabor wave kernel function which can capture local structure informations corresponding to spatial and frequency to express the most useful local features of faces, the thesis use the Gabor response of face image to represent face features. In order to deduce the high dimensions of the Gabor features, the basis of researching the advantages of both the principal component analysis and linear discriminant analysis, two feature extraction methods are put forward and examined on simple classifier and SVM classifier. The results show that GP feature extraction method on the SVM classifier obtains the better result.