— Real-time face recognition by computer systems is required in many commercial and security applications because it is the only way to protect privacy and security in the sea of peoples. On the other hand, face recognition generates huge amounts of data in real-time. Filtering out meaningful data from this raw data with high accuracy is a complex task. Most of the existing techniques primarily focus on the accuracy aspect using extensive matrix-oriented computations. Efficient realizations primarily reduce the computational space using eigenvalues. On the other hand, an eigenvalues oriented evaluation has minimum time complexity of O (n3 ), where n is the rank of the covariance matrix; the computation cost for co-variance generation is extra. Our frequency distribution curve (FDC) technique avoids matrix decomposition and other high computational matrix operations. FDC is formulated with a bias towards efficient hardware realization and high accuracy by using simple vector operation...
I. Sajid, Sotirios G. Ziavras, M. M. Ahmed