A compact CMOS face detection architecture based on shunting inhibitory convolutional neural networks

Authors

XiaoXiao Zhang, Hong Kong University of Science & Technology
Amine Bermak, Hong Kong University of Science & Technology
Farid Boussaid, University of Western Australia
Abdesselam Bouzerdoum, University of WollongongFollow

RIS ID

26564

Publication Details

X. Zhang, A. Bermak, F. Boussaid & A. Bouzerdoum, "A compact CMOS face detection architecture based on shunting inhibitory convolutional neural networks," in Proceedings of 4th IEEE International Symposium on Electronic Design, Test and Applications (DELTA-2008), 2008, pp. 374-377.

Abstract

In this paper, we present a compact, low cost, real-time CMOS hardware architecture for face detection. The proposed architecture is based on a VLSI-friendly implementation of Shunting Inhibitory Convolutional Neural Networks (SICoNN). Reported experimental results show that the proposed architecture can detect faces with 93% detection accuracy at 5% false alarm rate. A VLSI Systolic architecture was considered to further optimize the design in terms of execution speed, power dissipation and area. Potential applications of the proposed face detection hardware include consumer electronics, security, monitoring and head-counting.