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VFSC: A Very Fast Sparse Clustering to Cluster Faces from Videos

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Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10117))

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Abstract

Face clustering is a task to partition facial images into disjoint clusters. In this paper, we investigate a specific problem of face clustering in videos. Unlike traditional face clustering problem with a given collection of images from multiple sources, our task deals with set of face tracks with information about frame ID. Thus, we can exploit two kinds of prior knowledge about the temporal and spatial information from face tracks: sequence of faces in the same track and contemporary faces in the same frame. We utilize this forehand lore and characteristic of low rank representation to introduce a new light weight but effective method entitled Very Fast Sparse Clustering (VFSC). Since the superior speed of VFSC, the method can be adapted into large scale real-time applications. Experimental results with two public datasets (BF0502 and Notting-Hill), on which our proposed method significantly breaks the limits of not only speed but also accuracy clustering of state-of-the-art algorithms (up to 250 times faster and 10% higher in accuracy), reveal the imminent power of our approach.

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Authors and Affiliations

  1. University of Science, VNU-HCMC, Ho Chi Minh City, Vietnam

    Dinh-Luan Nguyen & Minh-Triet Tran

Authors
  1. Dinh-Luan Nguyen
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  2. Minh-Triet Tran
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Correspondence to Dinh-Luan Nguyen .

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Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University, Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Nguyen, DL., Tran, MT. (2017). VFSC: A Very Fast Sparse Clustering to Cluster Faces from Videos. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10117. Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_31

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  • DOI: https://doi.org/10.1007/978-3-319-54427-4_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54426-7

  • Online ISBN: 978-3-319-54427-4

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