Gender classification in live videos


Gender classification in live videos

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Gender classification in live videos

In this project,is gender classification in live videos.Human facial gender classification is an important task in live videos. It is still challenging in real applications due to motion blur, object occlusion and extreme illumination in real live videos. In this project, we propose the Multi-Branch Voting CNN framework.It detects and extracts the human face images in live videos, then apply adaptive brightness enhancement on each face image before feeding them into three CNN branches to settle the extreme illumination problem, and finally, we apply a majority voting scheme to reduce the influences from motion blur, object occlusion to further improve classification accuracy. Our method significantly outperforms the state-of-the-art solutions on the LFW dataset and our collected real-world live videos dataset called Gender Classification for Live Videos (GCLV), with high accuracy.

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