Recognition and detection of occluded faces by a neural network classifier with recursive data reconstruction
Takio Kurita, Mickael Pic, Takashi Takahashi · 2004
The paper describes how to improve the robustness to occlusions in face recognition and detection. We propose a neural network architecture which integrates an auto-associative neural network into a simple classifier. The auto-associative network is employed to recall the original face from a partially occluded face image and to detect the occluded regions in the input image. The original face can be reconstructed by replacing those regions with the recalled pixels. By applying this reconstruction process recursively, the integrated network is able to classify occluded faces robustly. To confirm the effectiveness of this method, we performed experiments on face image classification and face detection. It is shown that the classification performance is not decreased even if 20-30% of the face image is occluded.