Why recognition in a statistics-based face recognition system should be based on the pure face portion: A probabilistic decision-based proof

It is evident that the process of face recognition, by definition, should be based on the content of a face. The problem is: What is a "face"? Recently, a state-of-the-art statistics-based face recognition system, the PCA plus LDA approach, has been proposed (Swets and Weng, IEEE Trans. Pattern. Anal. Mach. Intell. 18 (8) (1996) 831-836). However, the authors used "face" images that included hair, shoulders, face and background. Our intuition tells us that only a recognition process based on a "pure" face portion can be called face recognition. The mixture of irrelevant data may result in an incorrect set of decision boundaries. In this paper, we propose a statistics-based technique to quantitatively prove our assertion. For the purpose of evaluating how the different portions of a face image will influence the recognition results, a hypothesis testing model is proposed. We then implement the above mentioned face recognition system and use the proposed hypothesis testing model to evaluate the system. Experimental results show that the influence of the "real"-face portion is much less than that of the nonface portion. This outcome confirms quantitatively that recognition in a statistics-based face recognition system should be based solely on the "pure" face portion.