Study On Single Sample Face Recognition Algorithm Based On Gaborwavelet Transform

With the rapid development of information technology, artificial intelligence is gradually changing the way people live. As an important branch of artificial intelligence, face recognition has been extensively reviewed and researched in recent years. In some practical application scenes, each person can only gets one face image as the training sample,but most of face recognition methods will get low recognition rate under the single sample condition, some of them will even simply no longer apply. This paper focuses on research of single sample face recognition, which starts from the Gabor wavelet transform,and devises effective solutions in the light of the variable factors such as illumination,facial expression and occlusion. The main work of this paper are listed as follows:1. First of all, the one-dimensional and two-dimensional Gabor wavelet transform are introduced in this paper, and the influence of the parameters of two-dimensional Gabor wavelet function on the Gabor kernel function are also analyzed, then the specific process of Gabor wavelet feature extraction of face images are presented.2. Aiming at the variable illumination of face image, the DCT coefficients with no DC component as features is used to remove the influence of linear light, then the extractive DCT features are combined with nearest neighbor classifier for classification.Meanwhile, the Gabor wavelet features and the nearest neighbor classifier is combined,which utilize the local describe characteristic of Gabor wavelets to enhance the adaptability of recognition approach against the nonlinear illumination variation. As the experiment proved, all the two methods have good robustness to a certain degree of illumination variation.3. According to the core idea of general learning method, Gabor wavelet features is adopted into adaptive linear regression classification(ALRC) scheme, which use the principle component analysis(PCA) to reduce the dimension of Gabor feature. And an improved method based on enhanced class model(ECM) is put forword. The method uses the auxiliary sample subset which has the most similar variations with test face images to extract the generic face variation characteristics, then constructs the class model with both the single training sample and the generic face variation characteristics. Finally, the linear regression classification approach is used to classify and recognize. Experiments shows that the methods proposed have good robustness to the facial variations caused by illumination, expression, and occlusion. In addition, the auxiliary sample images needed to construct the enhanced class model are less then that needed in ALRC.4. According to the basic idea of kernel method, the nonlinear regression classification method is improved, and the Gabor wavelet feature based adaptive nonlinear regression classification(GANRC) approach is proposed. The approach firstly extracts the Gabor wavelet feature of the test face image, then constructs the adaptive class model,and then maps the Gabor wavelet feature of the test sample and adaptive class model to a high dimensional space, finally determines the class which the test face image belongs to according to the minimum distance criterion. As the experiment proved, the method proposed has good robustness to the facial variations caused by illumination, expression,and occlusion, which can achieve high recognition accuracy rate in the case of relatively less feature dimension.