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

Rao Muhammad Anwer, Fahad Shahbaz Khan, Jorma Laaksonen

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

125 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
OtsikkoProceedings - 2018 International Conference on Biometrics, ICB 2018
KustantajaIEEE
Sivut90-97
Sivumäärä8
ISBN (elektroninen)9781538642856
DOI - pysyväislinkit
TilaJulkaistu - 13 heinäkuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Biometrics - Gold Coast, Austraalia
Kesto: 20 helmikuuta 201823 helmikuuta 2018
Konferenssinumero: 11

Conference

ConferenceInternational Conference on Biometrics
LyhennettäICB
MaaAustraalia
KaupunkiGold Coast
Ajanjakso20/02/201823/02/2018

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