A robust infrared face recognition method based on adaboost gabor features

Face recognition is one of the most successful applications in biometric authentication. However, methods reported in the literature are far from perfect and deteriorate ungracefully where lighting condition cannot be controlled. This paper presents a new robust method for face recognition under near infrared lighting condition based on AdaBoost Gabor features with linear discriminant analysis classification (ALGabor), which solves the problems produced by variations of illumination rightly, since the NIR images are insensitive to variations of environmental lighting, and Gabor wavelets can extract adequate features form the images. To gain the qualified NIR images, a device has been designed. Gabor wavelets are used to extract the features form the NIR images. Although Gabor feature vectors often have very high dimensions, a classifier has been trained using the AdaBoost algorithm to select the most representative feature. Compared with the huge number of features produced by typical Gabor wavelets, the classifier in this paper only selects hundreds of features, which saves computation and time cost significantly. The comparison between the results of the method in this paper and several classic algorithms proves the effectiveness of the proposed method.