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Biometrics is the use of science and technology to obtain behavioural or physical characteristics of a person for identification or verification purposes. The main motivation of storing a person’s biometric data is to reuse it for the recognition purposes. Due to the wide variety of biometric modalities, there are many data acquisition methods and analysis algorithms. In general, the biometrics of human beings are derived from physiological and behavioural features [21]. Fingerprints, irises and pupils, faces, palm print, voice, keystroke and gait are among the most widely used human biometrics.

Gait, voice and keystroke are typical behavioural features of biometrics. The main disadvantage of this kind of method is the requirement of high level feature detection algorithms. In addition, behavioural features are hard to obtain accurately and straightforwardly in real world applications and can also change over time. Therefore, the use of behavioural features as biometrics may not be reliable enough for high security applications. However, behavioural characteristics have found applications in soft biometrics.

Physiological biometrics technologies are widely used in both academia and industry as this kind of features is more discriminative and possess high within-class similarity and between- class dissimilarity. The face, fingerprints and iris are the most widely applied biometrics due to their high distinctness. Iris patterns have been proven to be unique for each person and, as a biometric, possess a very low false acceptance rate (FAR) [22]. However, simply comparing the FAR with other recognition approaches such as the face or fingerprints does not show the full picture. For example, iris recognition has a high false rejection rate (FRR) resulting from eye blinking and the use of contact lenses. Although the iris requires relatively high cost devices for real world applications, its high accuracy has promising application prospect in the scenarios with high security requirements.

In comparison, fingerprints have been widely used in access control systems such as building entrances, border controls, most smartphones and laptops due to its unparalleled efficiency and effectiveness. The main drawback of fingerprints is that they generally require candidates to

cooperate by making physical contact with the sensor surface. As a biometric modality, the human face is advantageous as it is an easily collectible and nonintrusive physiological biometric, compared with the iris and fingerprints [23]. The face has contactless acquisitions and can be widely applied to the surveillance systems [2].

Jain et al. define some good characteristics of biometric systems [24, 25]: for the data acquisition system, the first issue is convenience and efficiency, which require biometrics features in both probes and gallery should be easy and fast to obtain. Some complicated data acquisition devices might degrade the practicability of a biometric system. Also, safety is another key issue for data acquisition. The data acquisition process is completely private for a candidate subject and the biometric data should be securely stored or encrypted to avoid any spoofing attack. The extracted biometric features, either physical or behavioural, should be discriminative, consistent and robust, which can best preserve the within-class similarity and between-class dissimilarity. They require that the features are hard to be falsified by the subject and also with high consistency under various scenarios.

In general, there are two exclusive biometric application scenarios, verification and identification, as shown in Figure 2.1. For the verification or authentication scenario, a one-to- one matching of the probe data and the gallery data of the same identity is undertaken, in which the subject’s identity is compared with the claimed identity. A threshold is chosen to compare with the matching score and determine whether the target subject is accepted [25]. The verification performance is measured by the Receiver Operating Characteristic (ROC) curve which plots the FRR versus the FAR or the verification rate (1-FRR) versus the FAR [26]. The FAR is defined as the percentage of the probes that are wrongly recognized as the claimed person while the FRR is the percentage of the probes that are incorrectly rejected. Therefore, the ROC curve describes the trade-off between the FAR and the FRR. EER is the point where the FRR equals the FAR and it is the most commonly stated single number on the ROC curve. In addition, the FRR or verification rate at 0.1% FAR on the ROC is also used to evaluate the performance.

For the identification scenario, the input subject is compared with the whole database and the closest subject is found to denote its identity. The Cumulative Match Characteristic (CMC) curve is applied to evaluate the identification performance and plots the recognition rate (RR) versus the rank number, which summarizes the percentage of probes and galleries that are correctly matched [26]. R1RR is the most widely used performance metric on the CMC curve.

Figure 2.1: A description and comparison of face verification and identification system. For identification, the probe is identified by matching all the biometric data in the gallery and the output is the closest identity. For verification, the claimed identity of probe is verified by matching with its corresponding biometric data in the gallery.

In the all-to-all matching scenario, another method used for identification is ‘leave-one-out’ cross validation, which considers each capture as an individual and compares the target capture with the remaining captures by calculating a distance measure. The nearest subject denotes the probe’s identity and the Nearest Neighbours (NN) are widely used to produce the matching score or distance.