A framework for face classification under pose variations

Automatically verifying a person from a video frame or a digital image using computer application is known as a Face Recognition system. With changes in facial pose, face appearance changes drastically. Recognition of faces under pose variations is much more challenging. Now a day's recognizing human faces in unconstrained or wild environment is emerging as a critically important and technically challenging computer vision problem. Recently face recognition community is gradually shifting its focus on much more challenging unconstrained setting. A new unconstrained human face Database called as “My unconstrained Database” has been developed in this paper. A model based approach is used and the Moment based feature extraction techniques (Hu's, Zernike and Legendre Moments) are implemented on three different face databases containing different poses of the faces. This paper proposes a modified method called “Genetic Algorithm based Transfer Vectors” for generation of features of a frontal face from the features of different poses of image. Next, the generated frontal features are matched with the actual frontal features. Extracted feature are classified by three different methods: kNN classifier, LDA and Transform Vector with Distance Metric and finally Correct Recognition Rate is determined.