Gender Classification Using Local Directional Pattern (LDP)

Jabid, Taskeed; Kabir, Md. Hasanul; Chae, Oksam

doi:10.1109/icpr.2010.373

Cited by 120 publications

(68 citation statements)

References 11 publications

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“…LBP descriptor was employed in [27] in combination with intensity and shape features to get a multi-scale fusion approach, while Ylioinas et al [28] combined it with contrast information in order to achieve a more robust classification. To extract the most discriminative LBP features, Shan [29] proposed an AdaBoost selection method and many other variants have also been proposed to get more informative features, e.g., local Gabor binary mapping pattern [30,31] and local directional pattern [32].…”

Section: Gendermentioning

confidence: 99%

A study on different experimental configurations for age, race, and gender estimation problems

Carcagnì

Coco

Cazzato

et al. 2015

J Image Video Proc.

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This paper presents a detailed study about different algorithmic configurations for estimating soft biometric traits. In particular, a recently introduced common framework is the starting point of the study: it includes an initial facial detection, the subsequent facial traits description, the data reduction step, and the final classification step. The algorithmic configurations are featured by different descriptors and different strategies to build the training dataset and to scale the data in input to the classifier. Experimental proofs have been carried out on both publicly available datasets and image sequences specifically acquired in order to evaluate the performance even under real-world conditions, i.e., in the presence of scaling and rotation.

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Section: Gendermentioning

confidence: 99%

A study on different experimental configurations for age, race, and gender estimation problems

Carcagnì

Coco

Cazzato

et al. 2015

J Image Video Proc.

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show abstract

“…The average classification rate is calculated after repeating the above process for ten times. We have compared the proposed method in terms of classification rate with some widely-used local texture operators, namely local binary pattern (LBP) [23], local directional pattern [26], and local ternary pattern (LTP) [6]. Support vector machine was used for the classification task.…”

Section: Resultsmentioning

confidence: 99%

“…More recently, Sobel-LBP [19] has been proposed to improve the performance of LBP by applying Sobel operator to enhance the edge information prior to applying LBP for feature extraction. However, in uniform and near-uniform regions, [15], [26] employs a different texture encoding approach, where directional edge response values around a position is used instead of gray levels. Although this approach achieves better recognition performance than local binary pattern, LDP tends to produce inconsistent codes in uniform and near-uniform facial regions and is heavily dependent on the selection of the number of prominent edge directions [21], [27].…”

Section: Introductionmentioning

confidence: 99%

Gender Identification from Facial Image using Compound Local Binary Pattern (CLBP

Hossain¹,

Bhuyan²,

Ahmed³

2014

IJCA

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Automatic identification of male and female from facial image allows many useful applications in biometrics, surveillance, and human-computer interaction. This paper presents a robust face feature descriptor for gender classification from facial image. The proposed method is based on the compound local binary pattern (CLBP), an extension of the LBP texture operator. The CLBP operator exploits 2P bits to encode the information of a local neighborhood of P neighbors, where P bits are used to express the sign information and the other P bits are used to express the magnitude information of the differences between the center and the neighbor gray values. The performance of the proposed method has been evaluated using a large dataset comprising 1800 facial images collected from the FERET database. Extensive experiments with support vector machine classifier show the superiority of the CLBP feature descriptor against some well-known texture operators.

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“…In the current literature, most of the automatic gender classification systems use the same face database for obtaining the training and test sets [20,21,22]. In this case, the acquisition conditions of training and test images are exactly the same which is far from a realistic scenario.…”

Section: Introductionmentioning

confidence: 99%

Face gender classification: A statistical study when neutral and distorted faces are combined for training and testing purposes

Andreu

García-Sevilla

Mollineda

2014

Image and Vision Computing

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This paper presents a thorough study of gender classification methodologies performing on neutral, expressive and partially occluded faces, when they are used in all possible arrangements of training and testing roles. A comprehensive comparison of two representation approaches (global and local), three types of features (grey levels, PCA and LBP), three classifiers (1-NN, PCA+LDA and SVM) and two performance measures (CCR and d ′ ) is provided over single-and cross-database experiments. Experiments revealed some interesting findings, which were supported by three non-parametric statistical tests: when training and test sets contain different types of faces, local models using the 1-NN rule outperform global approaches, even those using SVM classifiers; however, with the same type of faces, even if the acquisition conditions are diverse, the statistical tests could not reject the null hypothesis of equal performance of global SVMs and local 1-NNs.

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Gender Classification Using Local Directional Pattern (LDP)

Cited by 120 publications

References 11 publications

A study on different experimental configurations for age, race, and gender estimation problems

A study on different experimental configurations for age, race, and gender estimation problems

Gender Identification from Facial Image using Compound Local Binary Pattern (CLBP

Face gender classification: A statistical study when neutral and distorted faces are combined for training and testing purposes

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