Low-Power Face Recognition Using Joint Optical and Electronic Deep Neural Networks

Power and energy constraints limit the implementation of deep face recognition algorithms on edge devices. To address this issue, we propose an electro-optic hybrid system, with an always-on optical neural network that continuously monitors faces in a given environment and activates deep face recognition when a face is detected. We adapt the system for a scenario similar to a smart door lock application, involving center-aligned, randomly appearing faces. Tested on the Labeled Faces in the Wild dataset, the proposed system achieves 95.8% accuracy with 16 features extracted from face images by principal components analysis and enables a remarkable 33.2% reduction in power usage compared to the same neural network on digital processors.