Digital architecture for real-time CNN-based face detection for video processing
In this paper, we propose a hardware computing architecture for face detection that classifies an image as a face or non-face. The computing architecture is first designed, modeled and tested in MATLAB Simulink using Xilinx block set and was later tested using a Virtex-6 FPGA ML605 Evaluation Kit. The system uses learned filters which were previously extracted by training on a set of face and non-face patterns. The system is fully feature based and does not require any assumptions on specific image processing techniques. The proposed approach takes an input image as a whole and passes it through different modules that apply sub-algorithms based on image convolution and sub-sampling followed by a non-linear signal processor containing artificial neurons. The architecture takes the form of a deep convolutional neural network (CNN) which can classify if a search window inside a picture contains a human face or not.