Multi-quantized local binary patterns for facial gender classification. (August 2016)

• Record Type: Journal Article
• Authors: Patel, Bhavik; Maheshwari, R.P.; Balasubramanian, R.
• Is Part Of: Computers & electrical engineering. Volume 54(2016)
• Page Start: 271
• Page End: 284
• Publication Date: 2016-08
• Subjects: Feature extraction -- Pattern classification -- Local binary patterns -- Gender classification; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.11.004
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Multi-level quantization scheme is proposed to enhance discrimination power of LBP. The proposed method utilizes both the sign and magnitude components from GLD. The results of the experiments validate the robustness of the proposed method. Thorough evaluation of four popular features (LBP, CLBP, ELBP and LTP) is presented. Abstract: This paper presents multi-quantized local binary patterns for facial gender classification. For encoding the gray level difference (GLD) between a reference pixel and its neighbors, local binary pattern employs a binary quantization which retains the sign of GLD but discards the magnitude information. To improve the discrimination capability, the proposed method utilizes both the sign and magnitude components by performing multi-level vector quantization of GLD. Each quantized level is then separately encoded to generate multiple local binary patterns. The proposed method is evaluated on four publicly available datasets (FERET, PAL, CASIA and FEI) through extensive experiments. Comparison of performance with various existing methods clearly demonstrated that the proposed method has advantages such as higher discrimination power, improved noise robustness and better generalization capability. Graphical abstract: