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Deep Learning Models for Face Recognition: A Comparative Analysis

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Deep Biometrics

Part of the book series: Unsupervised and Semi-Supervised Learning ((UNSESUL))

Abstract

Deep learning networks have established themselves as a promising model for face recognition. Their success is attributed towards multiple processing layers in order to learn data representations with several feature extraction levels. Convolutional neural networks have been present as the deep learning tool in almost all face recognition systems. The significant breakthrough made by DeepIDs, DeepFace, Face++, FaceNet, and Baidu has changed the entire investigation scope. The deep face recognition techniques leverage hierarchical architecture in order to learn discriminative face representation. It has improved the system’s performance appreciably which has led to the growth of several successful applications. In this chapter, comparative analysis for important deep learning models towards face recognition is presented. A multi-stage strategy is used in order to prepare the experimental datasets. These datasets are developed from readily available web knowledge sources. The datasets are used for model training as well as evaluation. The experimental results are presented considering their comparative analysis against benchmark datasets. Several deep face recognition issues and other open questions in deep face recognition are also discussed with directions towards future research.

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Author information

Authors and Affiliations

  1. Samsung R & D Institute Delhi, Noida, India

    Arindam Chaudhuri

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  1. Arindam Chaudhuri
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Editors and Affiliations

  1. School of Computing and Communication, InfoLab21, Lancaster University, Lancaster, UK

    Richard Jiang

  2. School of Information Technology, Deakin University, Waurn Ponds, VIC, Australia

    Chang-Tsun Li

  3. Electrical Engineering and Computer Science, School of Electronics, Queen’s University Belfast, Belfast, UK

    Danny Crookes

  4. Technical University of Denmark, Kgs. Lyngby, Denmark

    Weizhi Meng

  5. GREYC research Lab, ENSICAEN, Caen, France

    Christophe Rosenberger

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Chaudhuri, A. (2020). Deep Learning Models for Face Recognition: A Comparative Analysis. In: Jiang, R., Li, CT., Crookes, D., Meng, W., Rosenberger, C. (eds) Deep Biometrics. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-32583-1_6

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  • DOI: https://doi.org/10.1007/978-3-030-32583-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32582-4

  • Online ISBN: 978-3-030-32583-1

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