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Liveness and Threat Aware Selfie Face Recognition

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Selfie Biometrics

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Biometric-based human authentication can provide acceptable level of security to mobile devices such as a tablets and smartphones. Face is one of the most popular choices for biometrics on mobile device since the user can conveniently capture his face image by taking a selfie. Like any other security system, selfie face recognition is also vulnerable to attacks wherein an imposter can present photograph of a genuine user to gain an access to the mobile device. Liveness detection is an essential counter-measure to spoof attacks. In adverse scenarios, an attacker can physically force the user to provide his facial image to unlock the phone. In such cases, facial expression detection can act as a counter-measure. This chapter investigates face-based human recognition techniques on mobile devices and highlight methods having liveness and threat awareness.

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Author information

Authors and Affiliations

  1. Birla Institute of Technology and Science Pilani, Pilani Campus, Pilani, Jhunjhunu, 333031, Rajasthan, India

    Geetika Arora & Kamlesh Tiwari

  2. National Institute of Technical Teachers’ Training and Research (NITTTR) Kolkata, FC Block Sector 3 Salt Lake, Kolkata, 700106, India

    Phalguni Gupta

Authors
  1. Geetika Arora
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  2. Kamlesh Tiwari
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  3. Phalguni Gupta
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Corresponding author

Correspondence to Geetika Arora .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS, USA

    Ajita Rattani

  2. Department of Computer Science and Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO, USA

    Reza Derakhshani

  3. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Arun Ross

Appendix

Appendix

There are certain parameters that are used for evaluating the performance of a face recognition system. Some of the commonly used measures are listed below.

  • One-eye detection rate: It refers to the rate of correctly detected blinks to the total number of blinks in test data. In this, right and left eyes are calculated separately.

  • Two-eye detection rate: It is same as one-eye detection rate, but it accounts for the simultaneous blinks of both the eyes for one blink activity.

  • False Acceptance Rate (FAR) and False Rejection Rate (FRR): FAR is the likelihood of the system to accept an unauthorized user as an authorized one. FRR, on the other hand, indicates the possibility of the system rejecting an authorized person by considering it as an imposter.

  • Equal Error Rate: It refers to the value where FAR and FRR are equal and is used to determine their threshold values. The lower the value of EER, higher would be accuracy of a biometric system.

  • Half Total Error Rate (HTER): It is computed by averaging the false acceptance rate and false rejection rate.

  • Area Under the Curve (AUC): A receiver operating characteristic curve (ROC curve) represents a graphical plot of true positive rate (TPR) against the false positive rate (FPR) at different threshold settings. The area under the ROC curve (AUC) refers to the probability of a randomly chosen positive example being classified as positive with greater suspicion than a randomly selected negative example.

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Arora, G., Tiwari, K., Gupta, P. (2019). Liveness and Threat Aware Selfie Face Recognition. In: Rattani, A., Derakhshani, R., Ross, A. (eds) Selfie Biometrics. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-26972-2_9

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  • DOI: https://doi.org/10.1007/978-3-030-26972-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26971-5

  • Online ISBN: 978-3-030-26972-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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