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Two-stream part-based deep representation for human attribute recognition
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en
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8
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Proceedings - 2018 International Conference on Biometrics, ICB 2018, pp. 90-97
Abstract
Recognizing human attributes in unconstrained environments is a challenging computer vision problem. State-of-the-art approaches to human attribute recognition are based on convolutional neural networks (CNNs). The de facto practice when training these CNNs on a large labeled image dataset is to take RGB pixel values of an image as input to the network. In this work, we propose a two-stream part-based deep representation for human attribute classification. Besides the standard RGB stream, we train a deep network by using mapped coded images with explicit texture information, that complements the standard RGB deep model. To integrate human body parts knowledge, we employ the deformable part-based models together with our two-stream deep model. Experiments are performed on the challenging Human Attributes (HAT-27) Dataset consisting of 27 different human attributes. Our results clearly show that (a) the two-stream deep network provides consistent gain in performance over the standard RGB model and (b) that the attribute classification results are further improved with our two-stream part-based deep representations, leading to state-of-the-art results.
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| openaire: EC/H2020/780069/EU//MeMAD
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Anwer, R M, Khan, F S & Laaksonen, J 2018, Two-stream part-based deep representation for human attribute recognition. in Proceedings - 2018 International Conference on Biometrics, ICB 2018. IEEE, pp. 90-97, International Conference on Biometrics, Gold Coast, Queensland, Australia, 20/02/2018. https://doi.org/10.1109/ICB2018.2018.00024