Towards Multi-modal Sclera and Iris Biometric Recognition with Adaptive Liveness Detection

Abstract

Security breaches due to misidentification of an individual pose one of the greatest threats and challenges for today’s world. The use of biometrics can be quite promising in minimising this threat. Biometrics refers to the automatic authentication of individuals based on their physiological and behavioural characteristics. To date, various biometric systems have been proposed in the literature, among them biometric traits such as the face, iris, fingerprints, retina, gait, and vocal patterns are found to be distinctive to each and every person and are considered to be most reliable biometric identifiers. Regardless of the available biometrics traits, to date, no biometric system has been found to be a perfect, and which can be applied universally in a way that is robust/adaptive to change in different environmental conditions. Multimodal biometric systems were proposed in the late 1990’s to extend the range of biometric applicability. In a multimodal biometric system, two or more biometric identifiers are fused by an information fusion technique, thereby providing robustness for changing in a greater range of environmental conditions and enhancing other properties that an ideal biometric system should possess. Another important property that a biometric system should possess is a capability to distinguish between real and fake data. Although both the robustness of the system and capability to distinguish between a real and fake data should be incorporated into a single system, there is a trade-off. Therefore, due to the aforementioned research problems, this thesis addresses advancements in multimodal ocular biometrics using iris and sclera and also investigates the trade-off between robustness/adaptability and anti-spoofing/liveness detection (which is one method to distinguish between real and fake data). Biometrics traits that allow personal identification, eye traits offer a good choice of biometrics, as the eye offers a wide range of unique characteristics. The two common eye biometric identifiers that can be found in the literature are the iris and retina. Two more biometrics that are becoming popular nowadays are the sclera and the peri-ocular. The iris biometric is believed to be the most reliable eye biometric and that is why various commercial products based on this biometric are available; but the iris biometric used in an unconstrained scenario is still an open research area. The performance of iris biometrics with changes in the gaze angle of the eye can be affected highly. Therefore, due to this restriction, high user cooperation is required by persons with squinty eyes to get successfully identified in an iris biometric system. Identifying individuals with darker irises is another big challenge in iris recognition in the visible spectrum. To mitigate this problem, multi-modal eye biometrics was proposed by combining iris and sclera traits in the visible spectrum. However, in order to establish the concept of multimodal eye biometrics using the iris and sclera, it is first necessary to assess if sufficient discriminatory information can be gained from the sclera, further assessment in regards to its combination with the iris pattern and adaptiveness of the traits with respect to changes in environmental conditions, population, the data acquisition technique and time span. Multimodal biometrics using sclera and iris have not been extensively studied and little is known regarding their usefulness. So, the state-of-the-art related to it is not sufficiently mature and still in its infancy. This thesis concentrates on designing an image processing and pattern recognition module for evaluating the potential of the scleral biometric with regards to biometric accuracy. Thus, research is also carried out investigate usefulness of the sclera trait in combination with the iris pattern. Various, pre-processing techniques, segmentation, feature extraction, information fusion and classification techniques are employed to push the border of this multimodal biometrics. The latter half of the thesis concentrates on bridging the anti-spoofing technique liveliness with adaptiveness of biometrics. Traditional biometric systems are not equipped to distinguish between fake and real data that has been scanned in front of the sensors. As a result, they adhere to forgery attacks by intruders who can take the privilege of a genuine user. With the rising demand of involuntary or unmanned biometric systems in border security, flight checking, and other restricted zones, the incorporation of the automatic detection of forgery attacks is becoming very obvious. Adaptability of the system with respect to the change in the trait is another important aspect that this biometric system should be enriched with. As mentioned previously both the forgery detection method (termed as liveness detection in the literature of biometrics) and adaptability of the trait is necessary for a trusted involuntary biometric system, but initial studies in the literature exhibit it as a trade-off. Therefore to fulfil the gap, this thesis aimed to propose a new framework for software-based liveness detection, which is also associated to the adaptability of the trait. To fulfil the above-highlighted aim in the proposed framework, intra-class level (i.e. user level) liveness detection is introduced employing image quality-based features. Furthermore, to incorporate the adaptability of the trait, online learning-based classifiers are used. Initial investigation and experimental results solicit the use of the proposed framework for trusted involuntary biometric systems. Two new multi-angle eye datasets were developed and published as a part of the current research. The thesis also consists of contributions to other fields of pattern recognition such as wrist vein biometrics, multiscript signature verification and script identification.