Multiscale face recognition in cluttered backgrounds based on visual attention

Abstract

The human vision system can efficiently recognize multiscale objects in cluttered backgrounds. The scheme can be achieved with a visual attention mechanism by concentrating visual resources to the saliency area while ignoring other task-irrelevant areas. However, in the computer vision community, when recognizing multiscale faces in cluttered backgrounds, object detection modules are necessary to locate face regions and reduce the influence of complex backgrounds on the recognition model, which inevitably increases the computational complexity. Motivated by the human vision system, this study proposes the attention developmental network to recognize multiscale faces without using face detectors. A top-down attention mechanism is used to teach the network to focus on the face areas and ignore the backgrounds. An attention-based synapse maintenance mechanism is also introduced to further suppress the background pixels and improve the accuracy of face recognition. Comparative experiments show that our method can attain at least 13% of accuracy improvement over bionic neural networks and ResNet-based recognition networks on the same model scale with less training epochs.