Embedded facial image processing with Convolutional Neural Networks

This paper presents an embedded facial image analysis framework based on Convolutional Neural Networks (ConvNets). This robust framework has been proposed by Garcia, Delakis and Duffner on general purpose workstations without any constraints on computational and memory resources. We show that ConvNets, which consist of a pipeline of convolution and subsampling operations followed by a Multi Layer Perceptron, are particularly well suited for implementation on embedded processors. We present a set of high-level optimizations, such as automatic fractional transformation, convolution and subsampling fusion and memory requirement optimizations that can be applied to these algorithms without any loss in performance, leading to a speedup factor up to 700 compared to the reference implementation. This work leads to a face processing library able to handle the complete framework and its applications on mobile phones.