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DIEDA: discriminative information based on exponential discriminant analysis combined with local features representation for face and kinship verification

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Abstract

Face and kinship verification using facial images is a novel and challenging problem in computer vision. In this paper, we propose a new system that uses discriminative information, which is based on the exponential discriminant analysis (DIEDA) combined with multiple scale descriptors. The histograms of different patches are concatenated to form a high dimensional feature vector, which represents a specific descriptor at a given scale. The projected histograms for each zone use the cosine similarity metric to reduce the feature vector dimensionality. Lastly, zone scores corresponding to various descriptors at different scales are fused and verified by using a classifier. This paper exploits discriminative side information for face and kinship verification in the wild (image pairs are from the same person or not). To tackle this problem, we take examples of the face samples with unlabeled kin relations from the labeled face in the wild dataset as the reference set. We create an optimized function by minimizing the interclass samples (with a kin relation) and maximizing the neighboring interclass samples (without a kinship relation) with the DIEDA approach. Experimental results on three publicly available face and kinship datasets show the superior performance of the proposed system over other state-of-the-art techniques.

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Acknowledgments

This work was supported by the research grant from the Ministry of research and higher education (CERIST) of Algeria under Grant PNE number 40 and University of Louisville.

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Authors and Affiliations

  1. Department of Computer Engineering, Computer Science, University of Louisville, Louisville, KY, USA

    Rachid Aliradi & Adel S. Elmaghraby

  2. University of Sciences and Technology (USTHB) and CERIST, Algiers, Algeria

    Rachid Aliradi & Abdelkader Belkhir

  3. Department of Electrical Engineering, University of Mohamed Khider, Biskra, Algeria

    Abdelmalik Ouamane

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  1. Rachid Aliradi
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  2. Abdelkader Belkhir
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  3. Abdelmalik Ouamane
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  4. Adel S. Elmaghraby
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Correspondence to Rachid Aliradi.

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Aliradi, R., Belkhir, A., Ouamane, A. et al. DIEDA: discriminative information based on exponential discriminant analysis combined with local features representation for face and kinship verification. Multimed Tools Appl (2018). https://doi.org/10.1007/s11042-017-5572-2

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  • DOI: https://doi.org/10.1007/s11042-017-5572-2

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