City Research Online

Abstract

Biometric verification is the process of authenticating a person‟s identity using his/her physiological and behavioural characteristics. It is well-known that multimodal biometric systems can further improve the authentication accuracy by combining information from multiple biometric traits at various levels, namely sensor, feature, match score and decision levels. Fusion at match score level is generally preferred due to the trade-off between information availability and fusion complexity. However, combining match scores poses a number of challenges, when treated as a two-class classification problem due to the highly imbalanced class distributions. Most conventional classifiers assume equally balanced classes. They do not work well when samples of one class vastly outnumber the samples of the other class. These challenges become even more significant, when the fusion is based on user-specific processing due to the limited availability of the genuine samples per user. This thesis aims at exploring the paradigm of one-class classification to advance the classification performance of imbalanced biometric data sets. The contributions of the research can be enumerated as follows.

Firstly, a thorough investigation of the various one-class classifiers, including Gaussian Mixture Model, k-Nearest Neighbour, K-means clustering and Support Vector Data Description, has been provided. These classifiers are applied in learning the user-specific and user-independent descriptions for the biometric decision inference. It is demonstrated that the one-class classifiers are particularly useful in handling the imbalanced learning problem in multimodal biometric authentication. User-specific approach is a better alternative with respect to user-independent counterpart because it is able to overcome the so-called within-class sub-concepts problem, which arises very often in multimodal biometric systems due to the existence of user variation.

Secondly, a novel adapted score fusion scheme that consists of one-class classifiers and is trained using both the genuine user and impostor samples has been proposed. This method also replaces user-independent by user-specific description to learn the characteristics of the impostor class, and thus, reducing the degree of imbalanced proportion of data for different classes. Extensive experiments are conducted on the BioSecure DS2 and XM2VTS databases to illustrate the potential of the proposed adapted score fusion scheme, which provides a relative improvement in terms of Equal Error Rate of 32% and 20% as compared to the standard sum of scores and likelihood ratio based score fusion, respectively.

Thirdly, a hybrid boosting algorithm, called r-ABOC has been developed, which is capable of exploiting the natural capabilities of both the well-known Real AdaBoost and one-class classification to further improve the system performance without causing overfitting. However, unlike the conventional Real AdaBoost, the individual classifiers in the proposed schema are trained on the same data set, but with different parameter choices. This does not only generate a high diversity, which is vital to the success of r-ABOC, but also reduces the number of user-specified parameters. A comprehensive empirical study using the BioSecure DS2 and XM2VTS databases demonstrates that r-ABOC may achieve a performance gain in terms of Half Total Error Rate of up to 28% with respect to other state-of-the-art biometric score fusion techniques.

Finally, a Robust Imputation based on Group Method of Data Handling (RIBG) has been proposed to handle the missing data problem in the BioSecure DS2 database. RIBG is able to provide accurate predictions of incomplete score vectors. It is observed to achieve a better performance with respect to the state-of-the-art imputation techniques, including mean, median and k-NN imputations. An important feature of RIBG is that it does not require any parameter fine-tuning, and hence, is amendable to immediate applications.

Publication Type:	Thesis (Doctoral)
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Departments:	Doctoral Theses School of Science & Technology > Engineering School of Science & Technology > School of Science & Technology Doctoral Theses

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