Multi-View Gender Classification Using Multi-Resolution Local Binary Patterns and Support Vector Machines

Abstract

In this paper, we present a novel method for multi-view gender classification considering both shape and texture information to represent facial images. The face area is divided into small regions from which local binary pattern (LBP) histograms are extracted and concatenated into a single vector efficiently representing a facial image. Following the idea of local binary pattern, we propose a new feature extraction approach called multi-resolution LBP, which can retain both fine and coarse local micro-patterns and spatial information of facial images. The classification tasks in this work are performed by support vector machines (SVMs). The experiments clearly show the superiority of the proposed method over both support gray faces and support Gabor faces on the CAS-PEAL face database. A higher correct classification rate of 96.56% and a higher cross validation average accuracy of 95.78% have been obtained. In addition, the simplicity of the proposed method leads to very fast feature extraction, and the regional histograms and fine-to-coarse description of facial images allow for multi-view gender classification.

Learning local binary patterns for gender classification on real-world face images

“…As an efficient non-parametric method summarizing the local structure of an image, Local Binary Patterns (LBP) has been ex- ploited for face analysis [15]. For example, in [16,17], LBP was exploited for gender recognition on face images acquired under controlled conditions. In the existing work, LBP histograms are extracted from local facial regions as the region-level description, where the n-bin histogram is utilized as a whole.…”

Multi-view gender classification using symmetry of facial images

“…The MLGBP features, which are the input of the SVM classifiers, are derived by combining multi-resolution analysis, Gabor characteristic, and uniform LBP histograms [9,34].…”

Gender Classification Based on Support Vector Machine with Automatic Confidence

“…In this paper, we use gray, Gabor, local binary pattern (LBP) [15] [16], multiresolution local binary pattern (MLBP) [17], local Gabor binary pattern (LGBP) [18], and multi-resolution local Gabor binary pattern (MLGBP) approaches to extract the features of each facial image. Thereinto, the MLGBP feature as input of SVM [8] [9] [10] and SVMAC classifiers is derived by combining multi-resolution analysis, Gabor characteristic and uniform LBP histograms.…”