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Learned vs. Hand-Crafted Features for Pedestrian Gender Recognition

Abstract : This paper addresses the problem of image features selection for pedestrian gender recognition. Hand-crafted features (such as HOG) are compared with learned features which are obtained by training convolutional neural networks. The comparison is performed on the recently created collection of versatile pedestrian datasets which allows us to evaluate the impact of dataset properties on the performance of features. The study shows that hand-crafted and learned features perform equally well on small-sized homogeneous datasets. However, learned features significantly outperform hand-crafted ones in the case of heterogeneous and unfamiliar (unseen) datasets. Our best model which is based on learned features obtains 79% average recognition rate on completely unseen datasets. We also show that a relatively small convolutional neural network is able to produce competitive features even with little training data.
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https://hal.archives-ouvertes.fr/hal-01380429
Contributor : Grigory Antipov Connect in order to contact the contributor
Submitted on : Thursday, October 13, 2016 - 10:18:59 AM
Last modification on : Tuesday, February 2, 2021 - 2:26:02 PM

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Grigory Antipov, Sid-Ahmed Berrani, Natacha Ruchaud, Jean Luc Dugelay. Learned vs. Hand-Crafted Features for Pedestrian Gender Recognition. Conférence internationale, Oct 2015, Brisbane, Australia. ⟨10.1145/2733373.2806332⟩. ⟨hal-01380429⟩

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