Open Access Subscription or Fee Access
Face Detection and Reorganization using Neural Network Concept
Face recognition is the latest biometric technology which based on identification or verifying the person/user by his/her faces. The face recognition is done by using various algorithms for computing and processing the face for the specific purpose. We propose a new face recognition system, which automatically identifying a person/user from a video frame from the input video or video as live. In our model, we are using the concept of neural network concept to develop the algorithm called as Local Mapping analysis algorithm and we use cluster information’s (Neural Nets using statistical cluster information’s). By using this algorithm we can lead to eliminate the various problems that occurs in recognizing the face with some illumination variation, pose variations etc. By extracting some of the unique features like distance between eye, the shape of the cheekbones and the length between the jaw line etc.
Neural Network Concept, Statistical Cluster Information’s, Neural Nets Using Statistical Cluster Information’s, Illumination and Pose Variation, Mapping Analysis.
M.H.Hayes and A.U. Batur, “Linear Subspace for Illumination Robust Face recognition,” Dec. 2001.
P.N. Bellhumeur, J.P. Hespaasnha, and D.J. Kriegman, “Fisher-faces vs Eigen-faces:
Recognition Using The Class Specific and Linear Projection,”, July 1997.
P.Niyogi and M. Belkin, “Laplacian, Eigenmaps and Spectral Techniques for Embedding & Clustering”, 2001.
M. Bellkin and P. Niyogig, “Using Manifold Structures of the Partially Labeled Classification” , 2002.
M.Brand, “Charting a Manifold,” Neural Info.Processing System, 2002.
Vidin Sujith M, Vinoth R,Sathishkumar M "Illumination And Pose Variation Across Face Recognition Using Local Mapping Analysis" ijert, march- 2013.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.