One of the most successful applications of image analysis and understanding, face recognition has received significant attention. There are at least two reasons for the trend: the first is the wide range of commercial and law enforcement application and the second is the availability of feasible technologies. In general, few methods of face recognition are in practice, Feature based face recognition methods, Eigen Face Based, Line Based, Elastic Bunch Graph method and Neural Network based methods. All have their possibilities and features. In Neural Network approach automatic detection of eyes and mouth is followed by a spatial normalization of the images. The classification of the normalized images is carried out by hybrid Neural Network that combines unsupervised and supervised methods for finding structures and reducing classification errors respectively. The line-based is a type of image-based approach. It does not use any detailed biometric knowledge of the human face. These techniques use either the pixel-based bi-dimensional array representation of the entire face image or a set of transformed images or template sub-images of facial features as the image representation. An image-based metric such as correlation is then used to match the resulting image with the set of model images. In the context of image-based techniques, two approaches are there namely template-based and neural networks. In the template-based approach, the face is represented as a set of templates of the major facial features, which are then matched with the prototypical model face templates. Neural network-based image techniques use an input image representation that is the gray-level pixel-based image or transformed image which is used as an input to one of a variety of neural network architectures, including multilayer, radial basis functions and auto-associative networks.