3D face recognition from complement component range face images

Face and facial attributes represent meaningful definition about a variety of information to discriminate an individual from others and for developing a computational model for automatic face recognition purpose. However, in this work, selection of relevant features from newly created face space is the pivotal contribution of the authors. Here, authors have demonstrated a new face space `Complement Component´ that have been used to extract the four basic components along X, and Y axes in four directions. Later, authors have experimented the discriminative attributes from these face spaces for recognition purpose. Here, comparison of the proposed method has been reported by examining its success on two well accepted 3D face databases, namely: Frav3D and Texas3D. In case of 2D face images, it does not contain depth like information i.e. Z-values in X-Y plane through intensity values. Therefore, it has not been undertaken during this investigation.