Robust face recognition method based on SIFT features using Levenberg-Marquardt Backpropagation neural networks

Face recognition from image or video is a popular topic in biometrics research. It has many important practical applications, like surveillance and access control. It is concerned with the problem of correctly identifying facial images and assigning them to persons in a database. This paper proposes an efficient face recognition method based on Scale Invariant Feature Transform (SIFT) for feature extraction and using Levenberg-Marquardt Backpropagation (LMBP) neural network for classification. In this proposed method, we assign the extracted SIFT features of the face images as input vectors to our neural network instead of using just the raw data as the input. Experiments performed on the Yale face database show that the facial images can be recognized by the proposed face identification method efficiently. Also, the traditional face recognition algorithms are compared with the proposed algorithm to show its effectiveness.