Gender Classification Based on Fusion of Different Spatial Scale Features Selected by Mutual Information From Histogram of LBP, Intensity, and Shape

Tapia, Juan; Perez, Claudio A.

doi:10.1109/tifs.2013.2242063

Cited by 127 publications

(93 citation statements)

References 37 publications

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“…As mentioned above, most state-of-the-art GC approaches focus on the facial pattern. This is evidenced by the latest problem surveys [13,14], and recent results in major journals [2,9,10,15,16,17,18].…”

Section: Related Workmentioning

confidence: 95%

Descriptors and regions of interest fusion for in- and cross-database gender classification in the wild

Castrilln-Santana

Lorenzo-Navarro

Ramn-Balmaseda

2017

Image and Vision Computing

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Gender classification (GC) has achieved high accuracy in different experimental evaluations based mostly on inner facial details. However, these results do not generalize well in unrestricted datasets and particularly in cross-database experiments, where the performance drops drastically. In this paper, we analyze the state-of-the-art GC accuracy on three large datasets: MORPH, LFW and GROUPS. We discuss their respective difficulties and bias, concluding that the most challenging and wildest complexity is present in GROUPS. This dataset covers hard conditions such as low resolution imagery and cluttered background. Firstly, we analyze in depth the performance of different descriptors extracted from the face and its local context on this dataset. Selecting the bests and studying their most suitable combination allows us to design a solution that beats any previously published results for GROUPS with the Dago's protocol, reaching an accuracy over 94.2%, reducing the gap with other simpler datasets. The chosen solution based on local descriptors is later evaluated in a cross-database scenario with the three mentioned datasets, and full dataset 5-fold cross validation. The achieved results are compared with a Convolutional Neural Network approach, achieving rather similar marks. Finally, a solution is proposed combining both focuses, exhibiting great complementarity, boosting GC performance to beat previously published results in GC both cross-database, and full in-database evaluations.

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Section: Related Workmentioning

confidence: 95%

Descriptors and regions of interest fusion for in- and cross-database gender classification in the wild

Castrilln-Santana

Lorenzo-Navarro

Ramn-Balmaseda

2017

Image and Vision Computing

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“…Protocol Accuracy [19] LFW Subset 7443/13233 94.81% [20] LFW Subset 7443/13233 98.01% [7] LFW BEFIT protocol 97.23% [7] GROUPS Subset 15579/28231 84.55 − 86.61% [12] GROUPS Subset 22778/28231 86.4% [5] MORPH Subset 88% [17] MORPH Subset 97.1%…”

Section: Reference Datasetmentioning

confidence: 99%

Improving Gender Classification Accuracy in the Wild

Castrillón-Santana

Lorenzo-Navarro

Ramón-Balmaseda

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. In this paper, we focus on gender recognition in challenging large scale scenarios. Firstly, we review the literature results achieved for the problem in large datasets, and select the currently hardest dataset: The Images of Groups. Secondly, we study the extraction of features from the face and its local context to improve the recognition accuracy. Different descriptors, resolutions and classifiers are studied, overcoming previous literature results, reaching an accuracy of 89.8%.

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“…The problem of demographic estimation has been studied extensively in the literature [11][12][13][14][15][16][17][18][19][20][21][22][23]. Existing demographic estimation approaches can be grouped into three main categories: landmarks-based approaches, texture-based approaches, and appearance-based approaches.…”

Section: Demographic Estimationmentioning

confidence: 99%

“…However, manual annotation in facial landmark detection limits the usability of such approaches in automatic demographic estimation systems. Texture-based approaches, such as [16,17,20], utilize facial texture features, e.g., local binary patterns (LBP), Gabor, and biologically inspired features (BIF). Although used in many demographic estimation approaches, high feature dimensionality makes such approaches less efficient.…”

Section: Demographic Estimationmentioning

confidence: 99%

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

et al. 2018

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Demographic estimation of human face images involves estimation of age group, gender, and race, which finds many applications, such as access control, forensics, and surveillance. Demographic estimation can help in designing such algorithms which lead to better understanding of the facial aging process and face recognition. Such a study has two parts-demographic estimation and subsequent face recognition and retrieval. In this paper, first we extract facial-asymmetry-based demographic informative features to estimate the age group, gender, and race of a given face image. The demographic features are then used to recognize and retrieve face images. Comparison of the demographic estimates from a state-of-the-art algorithm and the proposed approach is also presented. Experimental results on two longitudinal face datasets, the MORPH II and FERET, show that the proposed approach can compete the existing methods to recognize face images across aging variations.

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Gender Classification Based on Fusion of Different Spatial Scale Features Selected by Mutual Information From Histogram of LBP, Intensity, and Shape

Cited by 127 publications

References 37 publications

Descriptors and regions of interest fusion for in- and cross-database gender classification in the wild

Descriptors and regions of interest fusion for in- and cross-database gender classification in the wild

Improving Gender Classification Accuracy in the Wild

Demographic-Assisted Age-Invariant Face Recognition and Retrieval

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