Gender Recognition Using Fusion of Local and Global Facial Features

Mirza, Anwar M.; Hussain, Muhammad; Almuzaini, Huda Abdulrahman; Muhammad, Ghulam; Aboalsamh, Hatim; Bebis, George

doi:10.1007/978-3-642-41939-3_48

Cited by 17 publications

(8 citation statements)

References 27 publications

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“…Proposed method 99.8 Moghaddam and Yang [18] 96.6 Mäkinen and Raisamo [22] 86.5 Baluja and Rowley [21] 97.1 Li et al [43] 95.8 Leng and Wang [44] 98.9 Lu and Shi [46] 94.8 Mirza et al [42] 98.1 Tapia and Pérez [43] 99.1 [18] 96.6 Mäkinen and Raisamo [22] 86.5 Baluja and Rowley [21] 97.1 Li et al [31] 95.8 Leng and Wang [43] 98.9 Lu and Shi [44] 94.8 Mirza et al [45] 98.1 Tapia and Pérez [23] 99.1…”

Section: Methods Overallmentioning

confidence: 99%

Real‐world gender classification via local Gabor binary pattern and three‐dimensional face reconstruction by generic elastic model

Moeini¹,

Faez²,

Moeini

2015

IET Image Processing

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In this study, a novel method is proposed for gender classification by adding facial depth features to texture features. Accordingly, the three-dimensional (3D) generic elastic model is used to reconstruct the 3D model from human face using only a single 2D frontal image. Then, the texture and depth are extracted from the reconstructed face model. Afterwards, the local Gabor binary pattern (LGBP) is applied to both facial texture and reconstructed depth to extract the feature vectors from both texture and reconstructed depth images. Finally, by combining 2D and 3D feature vectors, the final LGBP histogram bins are generated and classified by the support vector machine. Favourable outcomes are acquired for gender classification on the labelled faces in the wild and FERET databases based on the proposed method compared to several state-of-the-arts in gender classification.

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Section: Methods Overallmentioning

confidence: 99%

Real‐world gender classification via local Gabor binary pattern and three‐dimensional face reconstruction by generic elastic model

Moeini¹,

Faez²,

Moeini

2015

IET Image Processing

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show abstract

“…In an experimental study by Andreu et al [7], it was found that local approaches significantly outperform global approaches when face distortions (in the form of facial expressions or occlusions) and acquisition conditions differ in the training and test sets, implying better generalization ability. Combination of local and global features has also been explored, for example by fusing their features [20,88].…”

Section: Feature Extractionmentioning

confidence: 99%

A review of facial gender recognition

Tay

Goi

2015

Pattern Anal Applic

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Applications such as human-computer interaction, surveillance, biometrics and intelligent marketing would benefit greatly from knowledge of the attributes of the human subjects under scrutiny. The gender of a person is one such significant demographic attribute. This paper provides a review of facial gender recognition in computer vision. It is certainly not a trivial task to identify gender from images of the face. We highlight the challenges involved, which can be divided into human factors and those introduced during the image capture process. A comprehensive survey of facial feature extraction methods for gender recognition studied in the past couple of decades is provided. We appraise the datasets used for evaluation of gender classification performance. Based on the results reported, good performance has been achieved for images captured under controlled environments, but certainly there is still much work that can be done to improve the robustness of gender recognition under real-life environments.

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“…ANN: (Golomb et al, 1990), (Mirza et al, 2013), (Zhou & Li, 2016), (Jaswante et al, 2013). SVM: (Ignat & Coman, 2015), (Jain et al, 2005), (Deniz et al, 2011), (Shan, 2011)., (Chang et al, 2011), (Lee et al, 2013).…”

Section: Classifiersmentioning

confidence: 99%

Improving the SVM gender classification accuracy using clustering and incremental learning

Dagher

Azar

2019

Expert Systems

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Gender recognition has been playing a very important role in various applications such as human–computer interaction, surveillance, and security. Nonlinear support vector machines (SVMs) were investigated for the identification of gender using the Face Recognition Technology (FERET) image face database. It was shown that SVM classifiers outperform the traditional pattern classifiers (linear, quadratic, Fisher linear discriminant, and nearest neighbour). In this context, this paper aims to improve the SVM classification accuracy in the gender classification system and propose new models for a better performance. We have evaluated different SVM learning algorithms; the SVM‐radial basis function with a 5% outlier fraction outperformed other SVM classifiers. We have examined the effectiveness of different feature selection methods. AdaBoost performs better than the other feature selection methods in selecting the most discriminating features. We have proposed two classification methods that focus on training subsets of images among the training images. Method 1 combines the outcome of different classifiers based on different image subsets, whereas method 2 is based on clustering the training data and building a classifier for each cluster. Experimental results showed that both methods have increased the classification accuracy.

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Gender Recognition Using Fusion of Local and Global Facial Features

Cited by 17 publications

References 27 publications

Real‐world gender classification via local Gabor binary pattern and three‐dimensional face reconstruction by generic elastic model

Real‐world gender classification via local Gabor binary pattern and three‐dimensional face reconstruction by generic elastic model

A review of facial gender recognition

Improving the SVM gender classification accuracy using clustering and incremental learning

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