3D Face Recognition Using Radon Transform and Symbolic PCA

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 availability of improved 3D acquisition devices and processing algorithms. A 3D face image is represented by 3D meshes or range images which contain depth information. Range images have several advantages over 2D intensity images and 3D meshes. Range images are robust to the change of color and illumination, which are the causes for limited success in face recognition using 2D intensity images. In the literature, there are several methods for face recognition using range images, which are focused on the data acquisition and preprocessing stage only. In this paper, a new 3D face recognition technique based on symbolic Principal Component Analysis approach is presented. The proposed method transforms the 3D range face images using radon transform and then obtain symbolic objects, (i.e. interval valued objects) termed as symbolic 3D range faces. The PCA is employed to symbolic 3D range face image dataset to obtain symbolic eigen faces which are used for face recognition. The proposed symbolic PCA method has been successfully tested for 3D face recognition using Texas 3D Face Database. The experimental results show that the proposed algorithm performs satisfactorily with an average accuracy of 97% as compared to conventional PCA method and is efficient in terms of accuracy and detection time.