首页|A Convolutional Neural Network for Pedestrian Gender Recognition
A Convolutional Neural Network for Pedestrian Gender Recognition
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We propose a discriminatively-trained convolutional neural network for gender classification of pedestrians。 Convolutional neural networks are hierarchical， multilayered neural networks which integrate feature extraction and classification in a single framework。 Using a relatively straightforward architecture and minimal preprocessing of the images， we achieved 80。4% accuracy on a dataset containing full body images of pedestrians in both front and rear views。 The performance is comparable to the state-of-the-art obtained by previous methods without relying on using hand-engineered feature extractors。
Gender recognitionconvolutional neural network
Choon-Boon Ng、Yong-Haur Tay、Bok-Min Goi
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Universiti Tunku Abdul Rahman, Kuala Lumpur, Malaysia
NETL
