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- A FRAMEWORK FOR COMBINING ANCILLARY INFORMATION WITH PRIMARY BIOMETRIC TRAITS
- Ding, Yaohui
- Electronic Theses & Dissertations
Biometric systems recognize individuals based on their biological attributes such as faces, fingerprints and iris. However, in several scenarios, additional ancillary information such as the biographic and demographic information of a user (e.g., name, gender, age, ethnicity), or the image quality of the biometric sample, anti-spoofing measurements, etc. may be available. While previous literature has studied the impact of such ancillary information on biometric system performance, there is...
Show moreBiometric systems recognize individuals based on their biological attributes such as faces, fingerprints and iris. However, in several scenarios, additional ancillary information such as the biographic and demographic information of a user (e.g., name, gender, age, ethnicity), or the image quality of the biometric sample, anti-spoofing measurements, etc. may be available. While previous literature has studied the impact of such ancillary information on biometric system performance, there is limited work on systematically incorporatingthem into the biometric matching framework. In this dissertation, we develop a principled framework to combine ancillary information with biometric match scores. The incorporation of ancillary information raises several challenges. Firstly, ancillary information such as gender, ethnicity and other demographic attributes lack distinctiveness and can be used to distinguish population groups rather than individuals. Secondly, ancillaryinformation such as image quality and anti-spoof measurements may have different numerical ranges and interpretations. Further, most of the ancillary information cannot be automatically extracted without errors. Even the direct collection of ancillary informationfrom subjects may be susceptible to transcription errors (e.g., errors in entering the data). Thirdly, the relationships between ancillary attributes and biometric traits may not be evident. In this regard, this dissertation makes three contributions. The first contribution entails the design of a Bayesian Belief Network (BBN) to model the relationship between biometric scores and ancillary factors, and exploiting the ensuing structure in a fusion framework.The ancillary information considered by the network includes image quality and anti-spoof measures. Experiments convey the importance of explicitly incorporating such information in a biometric system. The second contribution is the design of a Generalized AdditiveModel (GAM) that uses spline functions to model the correlation between match scores and ancillary attributes, and then learns a transformation function to normalize the match scores prior to fusion. The resulting framework can also be used to predict in advance iffusing match scores with certain demographic attributes is beneficial in the context of a specific biometric matcher. Experiments indicate that the proposed method can be used to significantly improve the recognition accuracy of state-of-the-art face matchers. The thirdcontribution is the design of an ensemble of One Class Support Vector Machines (OC-SVMs) to combine multiple anti-spoofing measurements in order to mitigate the concerns associated with the issue of “imbalanced training sets” and “insufficient spoof samples” encountered by conventional anti-spoofing algorithms. In the proposed method, the spoof detection problem is formulated as a one-class problem, where the focus is on modeling a real fingerprint using multiple feature sets. The one-class classifiers corresponding to these multiple feature sets are then combined to generate a single classifier for spoof detection. Experimental results convey the importance of this technique in detecting spoofs made of materials that were not included in the training data. In summary, this dissertation seeks to advance our understanding of systematically exploiting ancillary information in designing effective biometric recognition systems by developing and evaluating multiple statistical models