Face Recognition based on Singular Value Decomposition Linear Discriminant Analysis Method
Manisha Deswal, Neeraj Kumar, Neeraj Rathi, M-Tech Scholar · 2014
In this paper, we are presenting the face recognition techniques based on the linear discriminant analysis method. The recognition of human faces is quite complex. The human face is full of information but working with all the information is time consuming and less efficient. It is better get unique and important information and discards other useless information in order to make system efficient. We implement Fischer face Singular Value Decomposition- Linear Discriminant Analysis (SVD-LDA) method where we have added Singular value decomposition (SVD) in comparison to Eigen-value decomposition (EVD) to reduce the time complexity and Euclidean distance in face space. Recognition is performed by projecting a new face image into the subspace spanned by the Eigen faces and then classifying the face by comparing its position in the face space with the positions of known individuals. The Results reveals that the efficiency of the Singular Value Decomposition-Linear Discriminant Analysis method is better than the other existing face recognition techniques. Results also shows that the time complexity is reduce to a great extant with Linear Discriminant Analysis method for face recognition.