Proceedings ArticleDOI
Gender Recognition in Non Controlled Environments
Agata Lapedriza,M.J. Maryn-Jimenez,Jordi Vitrià +2 more
- Vol. 3, pp 834-837
TLDR
A system to extract robust face features that can be applied to encode information from any zone of the face and that can been used for different face classification problems is presented.Abstract:
In most of the automatic face classification applications, images should be captured in natural environments, where partial occlusions or high local changes in the illumination are frequent. For this reason, face classification tasks in uncontrolled environment are still nowadays unsolved problems, given that the loss of information caused by these artifacts can easily mislead any classifier. We present in this paper a system to extract robust face features that can be applied to encode information from any zone of the face and that can be used for different face classification problems. To test this method we include the results obtained in different gender classification experiments, considering controlled and uncontrolled environments and extracting face features from internal and external face zones. The obtained rates show, on the one hand, that we can obtain significant information applying the presented feature extraction scheme and, on the other hand, that the external face zone can contribute useful information for classification purposes.read more
Citations
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Journal ArticleDOI
Learning local binary patterns for gender classification on real-world face images
TL;DR: This paper investigates gender recognition on real-life faces using the recently built database, the Labeled Faces in the Wild (LFW), and local Binary Patterns (LBP) is employed to describe faces, and Adaboost is used to select the discriminative LBP features.
Journal ArticleDOI
The Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition
TL;DR: It is shown that AGR consistently operationalises gender in a trans-exclusive way, and consequently carries disproportionate risk for trans people subject to it.
Journal ArticleDOI
Revisiting Linear Discriminant Techniques in Gender Recognition
TL;DR: The paper reviews the state-of-the-art in gender classification, giving special attention to linear techniques and their relations, and proves that Linear Discriminant Analysis on a linearly selected set of features also achieves similar accuracies.
Proceedings ArticleDOI
Soft biometric classification using periocular region features
TL;DR: It is shown that fusion of the soft biométrie information obtained from the classification approach with the texture based periocular recognition approach results in an overall performance improvement.
Posted Content
Vision-based Human Gender Recognition: A Survey
TL;DR: Based on the results, good performance have been achieved for datasets captured under controlled environments, but there is still much work that can be done to improve the robustness of gender recognition under real-life environments.
References
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Journal ArticleDOI
Learning gender with support faces
Baback Moghaddam,Ming-Hsuan Yang +1 more
TL;DR: Nonlinear support vector machines are investigated for appearance-based gender classification with low-resolution "thumbnail" faces processed from the FERET (FacE REcognition Technology) face database, demonstrating robustness and stability with respect to scale and the degree of facial detail.
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