Multi orientation performance of feature extraction for human head recognition

The main component for head recognition is a feature extraction. One of them as our novel method is histogram of transition. In this paper we evaluate multi orientation performance of this feature for human head detection. The input images are head and shoulder image with angle of 315°, 330°, 345°, 15°, 30° and 45°. We use SVM classifier to recognize the input image as a head or non head, which is trained by using normal orientation (0°) images. For comparison, we compare the recognition rate with the existing method of feature extraction, i.e. Histogram of Oriented Gradient (HOG) and Linear Binary Pattern (LBP). The experimental results show our feature more robust than the existing feature.