Abstract
Recent researches have shown closely related evidence between the human individual social behavior and precisely measurable facial features. The Facial-width-to-height ratio (FWHR) has become quite an interesting topic concerning human aggressive behavior. Recent studies presented evidence showing that the precise measurement of FWHR can be used to predict human aggressive behavior based on facial landmark extraction. In this paper, the Facial-width-to-height ratio is extracted and analyzed among men, women, and children using the recently presented Convolutional Experts Constrained Local Model (CE-CLM). Then, extracted features are used to train the Numeral Virtual Generalizing Random Access Memory (NVG-RAM) pattern recognition technique. The results show promising clues in depending on this feature extraction method for the Facial-width-to-height ratio, and depending on SVG-RAM classifier for aggressive behavior. Moreover, the proposed method is less susceptible to facial rotation error ensuring accurate FWHR extraction.
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