Real-time face recognition by computer systems is required in many commercial and security applications since it is the only way to protect privacy and security. 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 computationally intensive matrix operations. FDC is formulated with a bias towards efficient hardware realization and high accuracy by using simple vector operations. FDC requires pattern vector (PV) extraction from an image within O (n2) time. Our enhanced FDC-based architecture proposed in this paper further shifts a computationally expensive component of FDC to the offline layer of the system, thus resulting in very fast online evaluation of the input data. Furthermore, efficient online testing is pursued as well using an adaptive controller (AC) for PV classification utilizing the Euclidian vector norm length. The pipelined AC architecture adapts to the availability of resources in the target silicon device. Our implementation on an XC5VSX50t FPGA demonstrates a high accuracy of 99% in face recognition for 400 images in the ORL database, generally requiring less than 200 nsec per image.
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