Real-Time Face-Detection Engine for Robustness to Variable Illumination and Rotated Faces

Abstract

In this paper, we proposes a novel hardware architecture and FPGA implementation method of high performance real-time face-detection engine for robustness to variable illumination and rotation. The proposed face detection algorithm improved its performance by using MCT (Modified Census Transform), rotation transformation and AdaBoost learning algorithm. For implementation, we used a QVGA class camera, LCD display, and Virtex5 LX330 FPGA made by Xilinx Corporation. The verification results showed that it is possible to detect at least 32 faces in a wide variety of sizes at a maximum speed of 43 frames per second in real time. This finding can be applied to artificial intelligence robots for human recognition, conventional security systems for identity certification, and cutting-edge digital cameras using image processing techniques.