3D Face Recognition based on Radon Transform, PCA, LDA using KNN and SVM

Biometrics (or biometric authentication) refers to the identification of humans by their characteristics or traits. Bio-metrics is used in computer science as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance. Biometric identifiers are the distinctive, measurable characteristics used to label and describe individuals. 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. In the previous research works, there are several methods for face recognition using range images that are limited to the data acquisition and pre-processing 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 three publicly available databases, namely, Bhosphorus, Texas and CASIA 3D face databases. The experimental results are shown 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 Eigen faces of PCA and 5 LDA components lead to an average recognition rate of 99.20% using SVM classifier.