Rotation Invariant Face Detection using Spectral Histograms and Support Vector Machines

This paper presents a face detection method that detects faces with arbitrary rotation in the image plane. In this method, images are represented using a spectral histogram representation consisting of marginal distributions of filtered images. A support vector machine with an R.B.F. kernel is chosen as the classifier, which is trained on 4500 face and 8000 non-face images. The choice of filters allows a large degree of rotation invariance and by shuffling the marginals of certain filters, invariance to arbitrary rotation is achieved. A distinctive advantage of our method is that the invariance is achieved largely through the underlying representation while in other methods the invariance is typically achieved by detecting faces at a large number of different angles. The proposed method is tested on standard data sets and comparisons with other methods show that our method gives the best detection performance with respect to detection rate and false positives.