Two subspace methods to discriminate faces and clutters

Dimension reduction via linear subspace is very important in image pattern detection and recognition. This paper presents two new methods of dimension reduction and develops algorithms to locate human faces in gray-scale still images. The first technique develops eigenface subspace and eigenclutter subspace which represent faces and clutters respectively. The second technique chooses a common subspace to maximize the Bhattacharyya distance of two Gaussian distributions. Compared with the first method, the second method is more computationally efficient with slightly higher error rate. Our simulation result indicates that both methods outperform conventional template-based methods such as matched filter and eigenface methods.