HOS image similarity measure for human face recognition
Document Type
Conference Proceeding
Publisher
Institute of Electrical and Electronics Engineers Inc.
School
School of Engineering
RAS ID
29440
Abstract
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.
DOI
10.1109/CompComm.2017.8322814
Access Rights
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Comments
Shnain, N. A., Lu, S. F., & Hussain, Z. M. (2017, December). HOS image similarity measure for human face recognition. In Computer and Communications (ICCC), 2017 3rd IEEE International Conference on (pp. 1621-1625). IEEE. Available here