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Evaluation of Descriptors and Distance Measures on Benchmarks and First-Person-View Videos for Face Identification

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Computer Vision - ACCV 2014 Workshops (ACCV 2014)

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Abstract

Face identification (FI) has made significant amount of progress in the last three decades. Its application is now moving towards wearable devices (like Google Glass and mobile devices) leading to the problem of FI on first-person-views (FPV) or ego-centric videos for scenarios like business networking, memory assistance, etc. In the existing literature, performance analysis of various image descriptors on FPV data are little known. In this paper, we evaluate four popular image descriptors: local binary patterns (LBP), scale invariant feature transform (SIFT), local phase quantization (LPQ) and binarized statistical image features (BSIF) and ten different distance measures: Euclidean, Cosine, Chi square, Spearman, Cityblock, Minkowski, Correlation, Hamming, Jaccard and Chebychev with first nearest neighbor (1-NN) and support vector machines (SVM) as classifiers for FI task on both benchmark databases: FERET, AR, GT and FPV database collected using wearable devices like Google Glass (GG). Comparative analysis on these databases using various descriptors shows the superiority of BSIF with Cosine, Chi square and Cityblock distance measures using 1-NN as classifier over other descriptors and distance measures and even some of the current state-of-art benchmark database results.

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Notes

  1. 1.

    A test was performed with LBP and the Chi square distance measure with a different filter size and 1-NN as classifier, producing a classification accuracy of 60 %. The difference of 10 % (in Table 1) showcases the significance of fine-tuning the parameters in LBP. However, in this work, we are not focusing on fine tuning the parameters for LBP, but use same default parameters for all the experiments. This is also same for all other descriptors including SIFT, LPQ and BSIF.

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Authors and Affiliations

  1. Visual Computing Department, Institute for Infocomm Research, Singapore, Singapore

    Bappaditya Mandal & Liyuan Li

  2. Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Wang Zhikai & Ashraf A. Kassim

Authors
  1. Bappaditya Mandal
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  2. Wang Zhikai
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  3. Liyuan Li
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  4. Ashraf A. Kassim
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Correspondence to Bappaditya Mandal .

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Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    C.V. Jawahar

  2. Institue of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

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Mandal, B., Zhikai, W., Li, L., Kassim, A.A. (2015). Evaluation of Descriptors and Distance Measures on Benchmarks and First-Person-View Videos for Face Identification. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9008. Springer, Cham. https://doi.org/10.1007/978-3-319-16628-5_42

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  • DOI: https://doi.org/10.1007/978-3-319-16628-5_42

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