A novel approach for genetic face recognition

Face recognition plays an important role in Image processing, especially in the field of security authentication. It is used for authenticating a person in applications like security systems and identity verification. The challenge in implementation of face recognition is to tolerate the local variations in the facial expressions of an individual. There are several approaches for face recognition, of which Principal Component Analysis (PCA) is an effective algorithm. The importance of PCA lies, not only in reducing the dimensionality of the image, but, also in finding the significant features of the image. Computation of Eigen faces help in classifying the images to be genetic and non-genetic. The algorithm proposed intends to identify the genetically similar faces. The significant features are termed as Eigen faces, and they do not correspond to specific features such as eyes, nose and ears, of face. Using statistical measures the performance of algorithm shows that PCA is effective in extracting the features for genetic face recognition.