A study on the integerized implementation of PCA Recognition Algorithm

PCA 인식 알고리즘의 정수화 구현에 관한 연구

This paper proposes an integerized approach to solve PCA(Principal Component Analysis) feature extract procedure mainly used for the face recognition. A simple conversion to integer values has the risk to reduce the precision compared to that of the floating points operations. We integerize the PC variables by normalizing with the maximum of them, and show the efficiency of the proposed scheme by comparing the results to those of the float/double precisions. The integerized scheme is expected to be an efficient way for the real-time implementation of PCA's recognition stage, because integer operator is more desirable than floating point ones. Further research is to find a way to implement face detection and to measure the distances from the stored PCs for the full real-time face recognition.