A parallel algorithm of multiple face detection on multi-core system

Abstract

This work offers a graphics processing unit (GPU)-based system for real-time face recognition, which can detect and identify faces with high accuracy. This work created and implemented novel parallel strategies for image integral, computation scan window processing, and classifier amplification and correction as part of the face identification phase of the Viola-Jones cascade classifier. Also, the algorithm and parallelized a portion of the testing step during the facial recognition stage were experimented with. The suggested approach significantly improves existing facial recognition methods by enhancing the performance of two crucial components. The experimental findings show that the proposed method, when implemented on an NVidia GTX 570 graphics card, outperforms the typical CPU program by a factor of 19.72 in the detection phase and 1573 in the recognition phase, with only 2000 images trained and 40 images tested. The recognition rate will plateau when the hardware's capabilities are maxed out. This demonstrates that the suggested method works well in real-time.