Comparison of feature extraction methods for head recognition

Feature extraction plays an important role in head recognition. It transforms an original image into a specific vector to be fed into a classifier. An original image cannot be further processed directly. Raw information in an original image does not represent a specific pattern and a machine cannot understand that information. In this paper, we propose a novel feature extraction method for human head recognition and perform a comparison of the existing image features extraction methods using a static image. The existing features are HOG and LBP, and the proposed feature is a histogram of transition. A histogram of transition is based on calculation of a transition feature. A transition feature is to compute the location and the number of transitions from background to foreground along horizontal and vertical lines. So, this transition feature relies on foreground extraction. In design, the proposed feature has the number of arrays less than the existing features, and the computation of feature transition is simpler than the existing features. These conditions give the computation of the proposed feature faster than the computation of existing features. The recognition rates using the proposed feature are that the head recognition rate is 91% and the non-head recognition rate is 99.7%. The execution time is 0.077 ms. These performances show that the proposed feature can be used for real time application.