A wide variety of systems require reliable person recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that only a legitimate user and no one else access the rendered services. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. Face can be used as Biometrics for person verification. Face is a complex multidimensional structure and needs a good computing techniques for recognition. We treats face recognition as a two-dimensional recognition problem. A well-known technique of Principal Component Analysis (PCA) is used for face recognition. Face images are projected onto a face space that encodes best variation among known face images. The face space is defined by Eigen face which are eigenvectors of the set of faces, which may not correspond to general facial features such as eyes, nose, lips. The system performs by projecting pre extracted face image onto a set of face space that represent significant variations among known face images. The variable reducing theory of PCA accounts for the smaller face space than the training set of face. A Multire solution features based pattern recognition system used for face recognition based on the combination of Radon and wavelet transforms. As the Radon transform is in-variant to rotation and a Wavelet Transform provides the multiple resolution. This technique is robust for face recognition. The technique computes Radon projections in different orientations and captures the directional features of face images. Further, the wavelet transform applied on Radon space provides multire solution features of the facial images. Being the line integral, Radon transform improves the low-frequency components that are useful in face recognition
Bayesian face recognition and perceptual narrowing in face-space
