Linear Discriminant Analysis for 3D Face Recognition Using Radon Transform

Face recognition research started in the 70s and a number of algorithms/systems have been developed in the last decade. Three Dimensional (3D) human face recognition is emerging as a significant biometric technology. Research interest into 3D face recognition has increased during recent years due to the availability of improved 3D acquisition devices and processing algorithms. Three Dimensional face recognition also helps to resolve some of the issues associated with two dimensional (2D) face recognition. Since 2D systems employ intensity images, their performance is reported to degrade significantly with variations in facial pose and ambient illumination. The 3D face recognition systems, on the other hand, have been reported to be less sensitive to the changes in the ambient illumination during image capture that the 2D systems. In the previous works, there are several methods for face recognition using range images that are limited to the data acquisition and preprocessing stage only. In the present paper, we have proposed a 3D face recognition algorithm which is based on Radon transform, principal component analysis (PCA) and linear discriminant analysis (LDA). The radon transform (RT) is a fundamental tool to normalize 3D range data. The PCA is used to reduce the dimensionality of feature space, and the LDA is used to optimize the features, which are finally used to recognize the faces. The experimentation has been done using Texas 3D face database. The experimental results show that the proposed algorithm is efficient in terms of accuracy and detection time, in comparison with other methods based on PCA only and RT + PCA. It is observed that 40 eigenfaces of PCA and 5 LDA components lead to an average recognition rate of 99.16 %.