Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
