HOS image similarity measure for human face recognition

Facial recognition is a very hot topic due to its importance in security, surveillance systems, law enforcement, etc. Many methods are proposed to achieve high accuracy in face recognition but each method has its weaknesses as well as its points of strength. In this paper, we present an effective similarity measure to recognize human faces this measure is called (KSM). The proposed measure is based on the statistical properties of the image by using the high-order statistics (HOS), specifically kurtosis and skewness, for facial recognition. Performance evaluation was conducted using face images from AT&T database and FEI (Brazilian) database. The simulation results show that the proposed measure KSM outperforms the well-known Structural Similarity Index Measure (SSIM) and Feature Similarity Index Measure (FSIM) by the ability to detect similarity even under the difference in illumination, facial expression and pose variations.