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A Hybrid Deep Architecture for Face Recognition in Real-Life Scenario

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Computer Vision, Graphics, and Image Processing (ICVGIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10481))

Abstract

This article describes our recent study of a real-life face recognition problem using a hybrid architecture consisting of a very deep convolution neural network (CNN) and a support vector machine (SVM). The novel aspects of this study include (i) implementation of a really deep CNN architecture consisting of 11 layers to study the effect of increasing depth on recognition performance by a subsequent SVM, and (ii) verification of the recognition performance of this hybrid classifier trained by samples of a certain standard size on test face images of smaller sizes reminiscent to various real-life scenarios. Results of the present study show that the features computed at various shallow levels of a deep architecture have identical or at least comparable performances and are more robust than the deepest feature computed at the inner most sub-sampling layer. We have also studied a simple strategy of recognizing face images of very small sizes using this hybrid architecture trained by standard size face images and the recognition performance is reported. We obtained simulation results using the cropped images of the standard extended Yale Face Database which show an interesting characteristic of the proposed architecture with respect to face images captured in a very low intensity lighting condition.

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Authors and Affiliations

  1. Department of CSE, Indian Institute of Technology, Kanpur, India

    A. Sanyal

  2. CVPR Unit, Indian Statistical Institute, Kolkata, India

    U. Bhattacharya & S. K. Parui

Authors
  1. A. Sanyal
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  2. U. Bhattacharya
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  3. S. K. Parui
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Corresponding author

Correspondence to U. Bhattacharya .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology, Chittoor, India

    Snehasis Mukherjee

  2. GE Global Research, Bangalore, India

    Suvadip Mukherjee

  3. Indian Statistical Institute, Kolkata, India

    Dipti Prasad Mukherjee

  4. International Institute of Information Technology, Hyderabad, India

    Jayanthi Sivaswamy

  5. Indian Institute of Technology Bombay, Mumbai, India

    Suyash Awate

  6. CEDAR, Buffalo, New York, USA

    Srirangaraj Setlur

  7. International Institute of Information Technology, Hyderabad, India

    Anoop M. Namboodiri

  8. CSIR-CEERI, Pilani, Rajasthan, India

    Santanu Chaudhury

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Sanyal, A., Bhattacharya, U., Parui, S.K. (2017). A Hybrid Deep Architecture for Face Recognition in Real-Life Scenario. In: Mukherjee, S., et al. Computer Vision, Graphics, and Image Processing. ICVGIP 2016. Lecture Notes in Computer Science(), vol 10481. Springer, Cham. https://doi.org/10.1007/978-3-319-68124-5_11

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  • DOI: https://doi.org/10.1007/978-3-319-68124-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68123-8

  • Online ISBN: 978-3-319-68124-5

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