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Fundamentals and Advances in 3D Face Recognition

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Biometric-Based Physical and Cybersecurity Systems

Abstract

In this chapter, we focus on the fundamentals and advances in the research and commercial aspects of 3D face recognition systems. We consider security applications that have accelerated the growth of biometrics leading to both commercial and research-based system developments. A review of such systems and the factors influencing the choice of biometrics are considered. Advanced techniques in 3D face recognition are touched up on with emphasis on case studies based on different sensor-based databases. These sensors include the FRVT, Microsoft KINECT and stereo vision-based systems. The development of biometric systems needs to consider standards for interoperability, basis for evaluation through a benchmarking process as well as legal and privacy consideration which are covered in this chapter.

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Author information

Authors and Affiliations

  1. School of Engineering and Technology, University of Hertfordshire, Hatfield, UK

    Soodamani Ramalingam & Nguyen Trong Viet

  2. National Physical Laboratory, Teddington, UK

    Aruna Shenoy

Authors
  1. Soodamani Ramalingam
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  2. Aruna Shenoy
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  3. Nguyen Trong Viet
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Corresponding author

Correspondence to Soodamani Ramalingam .

Editor information

Editors and Affiliations

  1. ECE Department, Nazrabayev University, Astana, Kazakhstan

    Mohammad S. Obaidat

  2. Department of Electrical and Computer Engineering, University of Victoria – UVIC, Victoria, BC, Canada

    Issa Traore

  3. Department of Computer Science, Ryerson University, Toronto, ON, Canada

    Isaac Woungang

Appendix A: Performance Metrics

Appendix A: Performance Metrics

In this appendix, we consider the notations and terminologies commonly used to evaluate biometric systems [121].

  • Gallery and probe sets: For purpose of performance evaluation, the feature set 𝐹 is divided into partitions of gallery G that forms the database of templates of enrolled subjects and probe P that forms the set of query samples. Depending on the specific performance metric to be determined, the elements of the gallery and probe sets, g∊G and ∊P, respectively, will vary. For example, the probe set could be a subset of the gallery during the training phase of a face recognition system and mutually exclusive during the testing phase.

  • Identification: Identification in a biometric system is the process of determining the identification of an individual from the database. The identification process matches a probe as a query against the gallery and returns similarity scores, ∀g∊G. The scores are usually normalised in the range [0,1].

  • Verification is the process of confirming that a claimed identity is correct by comparing the probe with one or more enrolled templates.

  • Open-set and close-set identification: Identification is close-set if a person is assumed to be previously enrolled and open-set otherwise (as in the case of a watch list whose identity is not known previously).

  • False acceptance rate (FAR) : an empirical estimate of the probability that an impostor has been falsely verified to bear a correct identification.

  • False rejection rate (FRR) : an empirical estimate of the probability that a person with true identification has been falsely rejected by the system.

  • Equal error rate (EER) : The rate at which FAR = FMR.

  • Identity function : A function id(g) that returns the identity as an integer indexing the database templates and given by\( id:\mathcal{X}\longrightarrow \mathcal{U} \) where \( \mathcal{U} \) is a set of unique identities. Let Ug denote these set of identities in G and Up the identities in P. As mentioned before, for some testing conditions of training and testing phases, Ug ∩ Up = ∅.

  • Identification rate: Closed-set performance evaluation requires the sorting of similarity scores during a matching process of the probe against the gallery which are now in a natural increasing order of ranking. The identification rate I(k) is defined as the fraction of probes at rank k or below:

    $$ I(k)=\frac{\mid \left\{b|\operatorname{rank}(b)\le k,\kern0.75em {\forall}_{b\in B}\right\}\mid }{\mid {U}_{\mathrm{p}}\mid }, $$

    where |Up| is the size of the probe set.

  • Cumulative match curve (CMC) : The CMC chart is a plot of k vs I(k). It is a non-decreasing function. The example in [121] is quoted here. If there are 100 probes and a system has 50 outputs with 50 rank 1 outcomes, 40 rank 2 outcomes, 5 rank 3 outcomes, 3 rank 4 outcomes and 2 rank 5 outcomes, then the number of elements with rank k or less is {50, 90, 95, 98, 100} for ranks k = {1, 2, 3, 4, 5}, respectively. Hence, the identification rate is 50% for rank 1 performance, 90% for rank 2 performance and so on. As k increases, the identification rate increases and eventually attains 100%.

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Ramalingam, S., Shenoy, A., Viet, N.T. (2019). Fundamentals and Advances in 3D Face Recognition. In: Obaidat, M., Traore, I., Woungang, I. (eds) Biometric-Based Physical and Cybersecurity Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-98734-7_5

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