Face based person recognition mechanism using monogenic Binarized Statistical Image Features

These days, automated face recognition systems are hugely being applied in diverse applications ranging from personal use to border crossing. Feature extraction/representation is extremely vital module in any biometric systems, including face recognition. Thus, the main contribution of this paper is the proposition of a novel descriptor based on monogenic signal representation and Binarized Statistical Image Feature (BSIF) to extract quite distinctive relevant features from face image, named (M-BSIF). In fact, BSIF has not always efficient for face feature extraction, as it was not able to attain the best recognition rates. In order to enhance the capability of BSIF feature representation, our proposed feature description scheme, first applies band pass mechanism via log-Gabor filter on the image, then a monogenic filter is applied to decompose face image into three complementary parts, i.e., local amplitude, local phase, and local orientation. Next, BSIF is utilized to encode these complementary components in order to extract M-BSIF features. Experimental analyses on three publicly available databases (i.e., ORL database, AR database and JAFFE database) demonstrate the efficacy of the proposed M-BSIF descriptor. The proposed system outperforms a framework using only single BSIF.

Automated summary

This paper proposes a novel descriptor called M-BSIF for extracting distinctive and relevant features from face images. The proposed method combines monogenic signal representation and Binarized Statistical Image Feature (BSIF) to enhance the capability of face feature extraction. Experimental results on three public databases show that the proposed M-BSIF descriptor outperforms a framework using only single BSIF.