Seeing faces in noise: Exploring machine and human face detection processes by the reverse correlation method

In the present study, we aimed at investigating possible similarities (and discrepancies) between two major machine algorithms of face detection (AdaBoost and EigenFace) and human face detection processes. For this, we presented the 'false classification images' produced by the two face detection algorithms to human observers. Noise fields were fed into the two algorithms and images in which each algorithm falsely detected faces were collected. Those images were averaged and normalized to obtain false classification images. Human observers performed a psychophysical experiment to detect a face with the false classification images against random noise images. The face detection performance increased almost linearly as the number of averaged false detection images increase. Inverted images reduced the detection performance more with the images produced by EigenFace than those by AdaBoost. The present results suggest that both human and machine detection algorithms tended to make similar errors and therefore both AdaBoost and EigenFace are good approximation of human face processing.