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

Shan, Caifeng

doi:10.1016/j.patrec.2011.05.016

Cited by 322 publications

(236 citation statements)

References 23 publications

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“…Sometimes, simple features are pooled together as in (Kumar et al (2009)), where image intensities in RGB and HSV color spaces, edge magnitudes, and gradient directions were combined. More elaborated features include Haar-like wavelets (Shakhnarovich et al (2002)), Local Binary Patterns (LBPs) (Shan (2012)) or Gabor wavelets (Leng and Wang (2008)). These features work well and are robust to small illumination and geometric transformations.…”

Section: Related Workmentioning

confidence: 99%

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Local Deep Neural Networks for gender recognition

Mansanet

Albiol

Paredes

2016

Pattern Recognition Letters

116

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Deep Learning methods are able to automatically discover better representations of the data to improve the performance of the classifiers. However, in computer vision tasks, such us the gender recognition problem, sometimes it is difficult to directly learn from the entire image. In this work we propose a new model called Local Deep Neural Network (Local-DNN), which is based on two key concepts: local features and deep architectures. The model learns from small overlapping regions in the visual field using discriminative feed-forward networks with several layers. We evaluate our approach on two well-known gender benchmarks, showing that our Local-DNN outperforms other deep learning methods also evaluated and obtains state-of-the-art results in both benchmarks.

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Section: Related Workmentioning

confidence: 99%

“…For instance, the AdaBoost and the SVM algorithms have been widely used in the literature (Baluja and Rowley (2007); Shan (2012); Eidinger et al (2014)). In this spirit, an excellent comparison of gender recognition techniques using different methods can be found in Dago-Casas et al (2011).…”

Section: Related Workmentioning

confidence: 99%

Local Deep Neural Networks for gender recognition

Mansanet

Albiol

Paredes

2016

Pattern Recognition Letters

116

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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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“…Common features include Haar-like [10], HOG, LBP, edgelet, CSS, Covariance, and Integral channel [11]. HOG feature can reflect the appearance and contour of the target object, thus it is widely used in pedestrian detection [12,13].…”

Section: Related Workmentioning

confidence: 99%

Pedestrian Detection for Android Mobile Devices

Li¹,

Qu²,

Ma³

2018

dtcse

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With the popularity of Android mobile devices and rapid development of hardware performance, more computer vision algorithms can be implemented on Android mobile devices, such as object detection, face recognition, etc. This paper focuses on the problem of implementing pedestrian detection on Android mobile devices. Recently, the commonly used pedestrian detection algorithm is based on sliding window strategy which uses single or multi-features, such as HOG and Channel features. Due to the resource limitation of mobile devices, in this paper, we use HOG and LBP joint feature to achieve pedestrian detection. In order to improve the efficiency of the algorithm, we use the spatial pooling algorithm to process the HOG-LBP joint feature. Experiments on the Android mobile phone shows that this method not only has competitive accuracy but also improves the pedestrian detection efficiency on both INRIA dataset and mobile phone images.

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

Cited by 322 publications

References 23 publications

Local Deep Neural Networks for gender recognition

Local Deep Neural Networks for gender recognition

Improving Gender Classification Accuracy in the Wild

Pedestrian Detection for Android Mobile Devices

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