Neuro-Genetic and CCM based biometrical authentication through side-view of human-face

This paper presents the biometrical authentication process of any subject from side-view of human-face. Relevant geometric features of the human-face have been extracted using genetic-algorithm and connected component method from side-view of the human-face image. The present paper incorporates the frontal face images only for the formation of corpus. But for the biometrical authentication, side-view of the face has been analysed and connected component of face are calculated using neuro-genetic approach. Neuro-genetic means the combination of artificial neural network and genetic algorithm. The work has been carried out in two phases. In the first phase, formation of the FACE_MODEL as a corpus and calculation of connected component using frontal face images of the different subjects have been done. In the second phase, the model or the corpus has been used at the back-end for biometrical authentication using a proposed algorithm called NGBABA (Neuro-Genetic based Approach for Biometrical Authentication) and number of connected component of face is calculated using NGBBFSA (Neuro-Genetic Based Breadth First Search Algorithms). The authentication process has been carried out with the help of an unknown zero-degree (parallel to x-axis) oriented image. Hence relevant geometrical features and connected component with reducing orientation in image from ninety-degree to lower degree with 10-degree change have been matched with the corpus. The classification process of acceptance and rejection has been done after best-fit matching. The proposed algorithm has been tested with 10 subjects of varying age groups. The result has been found very satisfactory with the data sets.