Two-stream part-based deep representation for human attribute recognition

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Research units

  • Linköping University


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.


Original languageEnglish
Title of host publicationProceedings - 2018 International Conference on Biometrics, ICB 2018
Publication statusPublished - 13 Jul 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Biometrics - Gold Coast, Australia
Duration: 20 Feb 201823 Feb 2018
Conference number: 11


ConferenceInternational Conference on Biometrics
Abbreviated titleICB
CityGold Coast

    Research areas

  • Deep Learning, Human attribute Recognition, Part-based representation

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