New Fast Principal Component Analysis for Face Detection

Abstract

Principal component analysis (PCA) has different important applications, especially in pattern detection such as face detection and recognition. In real-time applications, response time must be as fast as possible. For this, we propose a new PCA implementation for fast face detection based on the cross-correlation in the frequency domain between the input image and eigenvectors (weights). Simulation results demonstrate that our proposal is faster than the conventional one, and experimental results for different images show good performance.