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

Moeini, Ali; Faez, Karim; Moeini, H.

doi:10.1049/iet-ipr.2014.0733

Cited by 15 publications

(9 citation statements)

References 42 publications

(60 reference statements)

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“…Detecting and aligning the face images automatically have been studied in the previous work of gender classification [21]. The texture descriptor-based methods such as Local Binary Pattern (LBP), different variants of LBP [33], Binarized Statistical Image Features (BSIF) [17], and Local Phase Quantization (LPQ) [1] has seen wide utility along with SVM classifier in the previous and as well as in the recent studies for improving gender classification accuracy [10,15,24,35,36] [2] to deal with the images collected in unconstrained environment. Also, significance of dictionary learning approaches such as Dictionary Learning for Gender Classification (DL-GC) and Separate Dictionary Learning for Gender Classification (SDL-GC) along with Sparse Representation Classifier (SRC) was employed to predict the gender from real face images acquired under a wide range of variations such as pose, expression, illumination, occlusion, etc.…”

Section: Visible Spectrummentioning

confidence: 99%

Collaborative Representation for Visible to Band Gender Classification Using Multi-spectral Imaging: Extensive Evaluations by Exploring 22 Photometric Normalization Methods

2021

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Facial biometric trait based on visible spectrum have been widely studied for gender classification in recent times due to its non-intrusive nature of image capture in a covert manner. Recently, with the advancement in sensing technology, multispectral imaging has gained significant attention in this direction. Although substantial studies related to facial gender classification can be seen in the literature independently based on visible and limited on multi-spectral imaging, the essence of gender classification based on visible to multi-spectral band is still an open and challenging problem due to varying spectral gap. In this paper, we present an extensive benchmarking study that employs 22 photometric normalization methods to bridge the gap between visible face images in the training set and facial band images in the testing set before performing the classification. Furthermore, for gender classification, we employed a robust Probabilistic Collaborative Representation Classifier (ProCRC) to learn the features from visible face images and then testing with the individual bands. We present the evaluation results based on visible and multi-spectral face database comprises of 82,650 sample face images belonging to six different illumination conditions. We present a benchmarking quantitative and qualitative average gender classification accuracy by employing 10-fold cross-validation approach independently across nine spectral bands, six different illumination conditions, and 22 photometric normalization methods. Furthermore, to present the essence of our proposed approach, we compare our evaluation results against Convolutional Neural Network (CNN) classifier. The experimental results show maximum gender classification accuracy of 95.3 ± 2.3% with our proposed approach, outperforming the state-of-the-art CNN demonstrating the potential of using photometric normalization methods for the improved performance accuracy.

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Section: Visible Spectrummentioning

confidence: 99%

Collaborative Representation for Visible to Band Gender Classification Using Multi-spectral Imaging: Extensive Evaluations by Exploring 22 Photometric Normalization Methods

2021

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“…We have applied our proposed method (AFIF 4 ) to unconstrained types of face images, i.e., frontal images, near-frontal images, non-frontal images, and images with large poses and occlusions. In literature, many gender classification methods are applied only to frontal or near-frontal face images [37,38,7,39,40]. For the sake of fair comparison, we report only results of methods using unconstrained types of images [16,9,41,42,43,8,18,12] and we omit results of methods using only frontal or near-frontal face images.…”

Section: Gender Classification Accuracymentioning

confidence: 99%

AFIF4: Deep gender classification based on AdaBoost-based fusion of isolated facial features and foggy faces

Afifi

Abdelhamed

2019

Journal of Visual Communication and Image Representation

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Gender classification aims at recognizing a person's gender. Despite the high accuracy achieved by state-of-the-art methods for this task, there is still room for improvement in generalized and unrestricted datasets. In this paper, we advocate a new strategy inspired by the behavior of humans in gender recognition.Instead of dealing with the face image as a sole feature, we rely on the combination of isolated facial features and a holistic feature which we call the foggy face. Then, we use these features to train deep convolutional neural networks followed by an AdaBoost-based score fusion to infer the final gender class. We evaluate our method on four challenging datasets to demonstrate its efficacy in achieving better or on-par accuracy with state-of-the-art methods. In addition, we present a new face dataset that intensifies the challenges of occluded faces and illumination changes, which we believe to be a much-needed resource for gender classification research.

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“…In the past few years, 3D related techniques rapidly developed on various applications [9, 10]. There are some studies estimating the body shape from clothing 3D body data.…”

Section: Introductionmentioning

confidence: 99%

Computational approach to body mass index estimation from dressed people in 3D space

Jiang

Shang

Guo

2020

IET Image Processing

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

Cited by 15 publications

References 42 publications

Collaborative Representation for Visible to Band Gender Classification Using Multi-spectral Imaging: Extensive Evaluations by Exploring 22 Photometric Normalization Methods

Collaborative Representation for Visible to Band Gender Classification Using Multi-spectral Imaging: Extensive Evaluations by Exploring 22 Photometric Normalization Methods

AFIF4: Deep gender classification based on AdaBoost-based fusion of isolated facial features and foggy faces

Computational approach to body mass index estimation from dressed people in 3D space

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