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The demographic classification, and gender recognition specifically, is a topic of considerable interest to researchers because of its importance in various applications, such as in areas of surveillance, face recognition, indexing videos, dynamic marketing studies, among other. This paper proposes a new way of carry through gender classification using a frequency vector based on local descriptors SIFT or SURF. The goal is to determine if the frequency vectors contain discriminant information. The training and validation of classification models will be based on Multi-PIE data, which contains face images taken under laboratory conditions, available with illumination changes, pose and expressions. For experimental development only considered images captured with normal room lighting, subjects with neutral expression and 11 changes of pose. The results validated that the proposed models contain discriminant information and also maintain a stable accuracy in gender classification on images with pose variations. The latter is extremely important, because in real life conditions are unlikely to acquire images of frontal faces, rather most will change perspective, rotation and illumination changes, therefore a robust model to these conditions is required. [ABSTRACT FROM AUTHOR]

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Copyright of Revista Brasileira de Linguística Antropológica is the property of Revista Brasileira de Linguística Antropológica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Brasileira de Linguística Antropológica is the property of Revista Brasileira de Linguística Antropológica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)