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- W2509675083 abstract "The secondary sex characteristics in faces of people are quite different due to variations of age, sex hormone, race and dress-up style. It is a very challenging work to build a gender recognition model for all kinds of people. This paper proposes to train a gender recognition model based on the deep convolutional network on a complete dataset. Our newly built complete dataset contains as many common variations of face images as possible. Based on this complete dataset, we design a very deep convolutional network as our gender classifier. We achieve an accuracy of 98.67% on the most challenging public database, labelled faces in the wild (LFW) [1]· We collect 10000 images from Internet and build a new dataset — Chinese wild database. Our model achieves the accuracy of 97.51% This indicates our model is robust to racial variation. In the above two experiments, our model achieves the state-of-the-art performances in the wild." @default.
- W2509675083 created "2016-09-16" @default.
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- W2509675083 date "2015-11-01" @default.
- W2509675083 modified "2023-10-16" @default.
- W2509675083 title "Deep learning for gender recognition" @default.
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- W2509675083 doi "https://doi.org/10.1109/ccoms.2015.7562902" @default.
- W2509675083 hasPublicationYear "2015" @default.
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