A Hardware-Friendly Low-Power Area-Efficient GMM-Based Analog Classifier For Skin Detection

Skin detection is the process of identifying skin pixels in a 2-D image or video. It is a vital part of today’s Computer Vision technology and widely spread among applications including medical diagnosis, cryptographic protection, localization tasks and human-machine interaction. To this end, this work proposes a novel, low power (31–35nW), low voltage (0.6V), area-efficient (2098µm2), GMM-Based analog classifier for skin detection. The architecture consists of bump circuits and a Lazzaro Winner-Take-All circuit. The presented classifier was designed and simulated in a TSMC 90nm CMOS process, using the Cadence IC Suite. To verify the proper operation of the proposed design, a real-world skin segmentation dataset is used. To confirm the circuit’s accuracy, post-layout simulation results were compared to a software-based implementation.