Radon Transform and Symbolic Linear Discriminant Analysis Based 3 D Face Recognition Using Knn and Svm

In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical application. Many recent events, such as terrorist attacks, have exposed the serious weaknesses in most sophisticated security systems. Automatic face recognition has long been established as one of the most active research areas in computer vision. In spite of the large number of developed algorithms, real-world performance of state-of-the-art methods has been disappointing. Three dimensional (3D) human face recognition is emerging as a significant biometric technology. Research interest in 3D face recognition has increased during recent years due to the availability of improved 3D acquisition devices and processing algorithms. In this paper, we have proposed a novel method for three dimensional (3D) face recognition using Radon transform and Symbolic LDA based features of 3D face images. In this method, the Symbolic LDA based feature computation takes into account the face image variations to a larger extent and has the advantage of dimensionality reduction. The experimental results have yielded 99.60% recognition accuracy using SVM with reduced computational cost, which compares well with other state-of-the-art methods.