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.