Dimensionality reduction of face images for gender classification

Buchala, Samarasena; Davey, Neil; Frank, Ray J.; Gale, Tim M.

doi:10.1109/is.2004.1344642

Cited by 22 publications

(19 citation statements)

References 11 publications

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“…In Villegas and Paredes (2011) the authors show a good comparison of different methods on a gender recognition problem among others. These techniques have been widely used because of their simplicity and effectiveness (Buchala et al (2004); Graf and Wichmann (2002)). However, they might not capture relevant information to represent a face in the gender recognition problem.…”

Section: Related Workmentioning

confidence: 99%

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%

Local Deep Neural Networks for gender recognition

Mansanet

Albiol

Paredes

2016

Pattern Recognition Letters

116

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“…Although there are several works on gender recognition of human face images [3] [8], there is no standard database or protocol for experimentation in this task. [6].…”

Section: Datasetmentioning

confidence: 99%

“…When class information was available, classical techniques focused on obtaining discriminative features (discriminant analysis) as well as a reduction of dimensionality. All these techniques have been widely used because of their simplicity and effectiveness [3].…”

Section: Introductionmentioning

confidence: 99%

Restricted Boltzmann Machines for Gender Classification

Mansanet

Albiol

Paredes

et al. 2014

Lecture Notes in Computer Science

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Abstract. This paper deals with automatic feature learning using Gaussian Restricted Boltzmann Machines (GRBM) for the problem of gender recognition in face images. The GRBM is presented together with some practical learning tricks to improve the learning capabilities and speedup the training process. The performance of the features obtained is compared against several linear methods using the same dataset and the same evaluation protocol. The results show a classification accuracy improvement compared with classical linear projection methods. Moreover, in order to increase even more the classification accuracy, we have run some experiments where an SVM is fed with the non-linear mapping obtained by the GRBM in a tandem configuration.

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“…In fact, an accuracy as good as 96% can be achieved with the hair concealed, facial hair removed and no makeup [6]. In recent years, a lot of effort has been spent on the statistical features based [4], [5], [16], [15], [10] approaches for gender classification. Most of them are based on the 2D intensity information.…”

Section: Introductionmentioning

confidence: 99%

Gender Classification using Shape from Shading

Smith

Hancock

2007

Procedings of the British Machine Vision Conference 2007

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The aim in this paper is to show how to use the 2.5D facial surface normals (needle-maps) recovered using shape from shading (SFS) to improve the performance of gender classification. We incorporate principal geodesic analysis (PGA) into SFS to guarantee the recovered needle-maps is a possible example defined by a statistical model. Because the recovered facial needlemaps satisfy data-closeness constraint, they not only give the facial shape information, but also combine the image intensity implicitly. Experiments show that this combination gives better gender classification performance than using facial shape or texture information alone.

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Dimensionality reduction of face images for gender classification

Cited by 22 publications

References 11 publications

Local Deep Neural Networks for gender recognition

Local Deep Neural Networks for gender recognition

Restricted Boltzmann Machines for Gender Classification

Gender Classification using Shape from Shading

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