Search Results for author: M. Castrillón-Santana

Found 2 papers, 0 papers with code

Descriptors and regions of interest fusion for gender classification in the wild. Comparison and combination with Convolutional Neural Networks

no code implementations24 Jul 2015 M. Castrillón-Santana, J. Lorenzo-Navarro, E. Ramón-Balmaseda

Selecting the bests and studying their most suitable combination allows us to design a solution that beats any previously published results for GROUPS with the Dago's protocol, reaching an accuracy over 94. 2%, reducing the gap with other simpler datasets.

Gender Classification General Classification +1

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