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Two-Stream Part-based Deep Representation for Human Attribute Recognition
Aalto Univ, Finland.
Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska fakulteten.
Aalto Univ, Finland.
2018 (engelsk)Inngår i: 2018 INTERNATIONAL CONFERENCE ON BIOMETRICS (ICB), IEEE , 2018, s. 90-97Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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.

sted, utgiver, år, opplag, sider
IEEE , 2018. s. 90-97
Serie
International Conference on Biometrics, ISSN 2376-4201
Emneord [en]
Deep learning; Human attribute recognition; Part-based representation
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-152842DOI: 10.1109/ICB2018.2018.00024ISI: 000449428100013ISBN: 978-1-5386-4285-6 (tryckt)OAI: oai:DiVA.org:liu-152842DiVA, id: diva2:1265191
Konferanse
11th IAPR International Conference on Biometrics (ICB)
Merknad

Funding Agencies|Academy of Finland [251170]; H2020-ICT project MeMAD [780069]; VR starting grant [2016-05543]; Nvidia; SSF

Tilgjengelig fra: 2018-11-22 Laget: 2018-11-22 Sist oppdatert: 2018-11-22

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