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Recognizing Altered Facial Appearances Due to Aging and Disguise

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Signal and Image Processing for Biometrics

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 292))

Abstract

This chapter focuses on recognizing faces with variations in aging and disguise. In the proposed approach, mutual information based age transformation algorithm registers the gallery and probe face images and minimizes the variations in facial features caused due to aging. Further, gallery and probe face images are decomposed at different levels of granularity to extract non-disjoint spatial features. At the first level, face granules are generated by applying Gaussian and Laplacian operators to extract features at different resolutions and image properties. The second level of granularity divides the face image into vertical and horizontal regions of different sizes to specifically handle variations in pose and disguise. At the third level of granularity, the face image is partitioned into small grid structures to extract local features. A neural network architecture based 2D log polar Gabor transform is used to extract binary phase information from each of the face granules. Finally, likelihood ratio test statistics based support vector machine classification approach is used to classify the granular information. The proposed algorithm is evaluated on multiple databases comprising of disguised faces of real people, disguised synthetic face images, faces with aging variations, and disguised faces of actors and actresses from movie clips that also have aging variations. These databases cover a comprehensive set of aging and disguise scenarios. The performance of the proposed algorithm is compared with existing algorithms and the results show that the performance of the proposed algorithm is significantly better than existing algorithms.

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Notes

  1. 1.

    Recognition can be either 1:1 matching (verification) or 1:N matching (identification). Note that, in this paper, recognition and verification are used interchangeably.

  2. 2.

    In the extensive experiments, it is observed that \(\varepsilon \) = 10 yields the best verification results with face images of size \(128 \times 128\).

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Acknowledgments

The author would like to thank Prof. A. Noore, Dr. A. Ross and Dr. M. Vatsa for discussions and feedback. The author also acknowledge the CVRL group from University of Notre Dame for providing the Notre Dame face database.

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Authors and Affiliations

  1. IIIT Delhi, Delhi, India

    Richa Singh

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  1. Richa Singh
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Correspondence to Richa Singh .

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Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Jacob Scharcanski

  2. IT-Instituto de Telecomunicações, University of Beira Interior, Covilhã, Portugal

    Hugo Proença

  3. Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, USA

    Eliza Du

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Singh, R. (2014). Recognizing Altered Facial Appearances Due to Aging and Disguise. In: Scharcanski, J., Proença, H., Du, E. (eds) Signal and Image Processing for Biometrics. Lecture Notes in Electrical Engineering, vol 292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54080-6_4

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  • DOI: https://doi.org/10.1007/978-3-642-54080-6_4

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  • Online ISBN: 978-3-642-54080-6

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