Face Detection: A Convolutional Neural Networks Approach

Automatic face detection in digital video is becoming a very important research topic, due to its wide range of applications, such as security access control, model-based video coding or content-based video indexing. In this paper, we present a connectionist approach for detecting and precisely localizing semi-frontal human faces in complex images, making no assumption on the content or the lighting conditions of the scene, neither on the size, the orientation, and the appearance of the faces. Unlike other systems depending on a hand-crafted feature detection stage, followed by a feature classification stage, we propose a convolutional neural network architecture designed to recognize strongly variable face patterns directly from pixel images with no preprocessing , by automatically synthesizing its own set of feature extractors from a large training set of faces. Moreover, the use of receptive fields, shared weights and spatial sub-sampling in such a neural model provides some degrees of invariance to translation, rotation, scale, and deformation of the face patterns. We present in details the optimized design of our architecture and our learning strategy. Then, we present the process of face detection using this architecture. Finally, we provide experimental results to demonstrate the robustness of our approach and its capability to precisely detect extremely variable faces in uncontrolled environment.