Abstract
Gender recognition is a relevant problem due to the number and importance of its possible application areas. The challenge is to achieve high recognition rates in the shortest possible time. Most studies are based on Local Binary Patterns (LBP) and its variants to estimate gender. In this paper, we propose the use of Binary Robust Independent Elementary Features (BRIEF), Oriented FAST and Rotated BRIEF (ORB) and Binary Robust Invariant Scalable Keypoints (BRISK) in gender recognition due to their good performance and speed. The aim is to show that ORB and BRISK are faster than LBP but allow to achieve similar recognition rates, which makes them suitable for realtime systems. For the best of our knowledge, it has not been studied in literature.
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CITATION STYLE
Iglesias, F. S., Buemi, M. E., Acevedo, D., & Jacobo-Berlles, J. (2014). Evaluation of keypoint descriptors for gender recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8827, pp. 564–571). Springer Verlag. https://doi.org/10.1007/978-3-319-12568-8_69
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