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Título : Exploring Local Deep Representations for Facial Gender Classification in Videos
Autor : Becerra Riera, Fabiola
Morales González, Annette
Méndez Vázquez, Heydi
Palabras clave : SOFT-BIOMETRICS;GENDER CLASSIFICATION VIDEO FACE ANALYSIS;DEEP LEARNING REPRESENTATION
Fecha de publicación : 2018
Editorial : Springer
Citación : Becerra-Riera F., Morales-González A., Méndez-Vázquez H. (2018) Exploring Local Deep Representations for Facial Gender Classification in Videos. In: Hernández Heredia Y., Milián Núñez V., Ruiz Shulcloper J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science, vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_12
Resumen : Gender recognition in videos is a challenging task that has received limited attention in recent years. To tackle this problem, we propose to explore the use of intermediate features of a Convolutional Neural Network (CNN) with a component-based face representation methodology. With this approach we intend to exploit the gender information provided by different face parts. The features extracted from video key frames are combined with two different strategies to preserve the temporal information, and Random Forest classifiers are employed to obtain a final gender prediction for a video sequence. Our results on the McGill and COX datasets show that our proposal outperforms the end-to-end CNN approach and, in the McGill dataset, 100% of accuracy was obtained.
URI : https://repositorio.uci.cu/jspui/handle/123456789/9451
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