The main component for head recognition is a feature extraction. One of them as our novel method is histogram of transition. This feature is relied on foreground extraction. In this paper we evaluate some pre-processing to get foreground extraction before we calculate the histogram of transition. We evaluate the performance of recognition rate in related with preprocessing of input image, such as color, size and orientation. We evaluate for Red-Green-Blue (RGB) and Hue-saturation-Value (HSV) color image; multi scale of 10×15 pixels, 20×30 pixels and 40×60 pixels; and multi orientation angle of 315o, 330o, 345o, 15o, 30o, and 45o. For comparison, we compare the recognition rate with the existing method of feature extraction, i.e. Histogram of Oriented Gradients (HOG) and Linear Binary Pattern (LBP). The experimental results show Histogram of Transition robust for changing of color, size and orientation angle.