Kar-Ann Toh

Alignment-Free Cancelable Fingerprint Templates Based on Local Minutiae Information

To replace compromised biometric templates, cancelable biometrics has recently been introduced. The concept is to transform a biometric signal or feature into a new one for enrollment and matching. For making cancelable fingerprint templates, previous approaches used either the relative position of a minutia to a core point or the absolute position of a minutia in a given fingerprint image. Thus, a query fingerprint is required to be accurately aligned to the enrolled fingerprint in order to obtain identically transformed minutiae. In this paper, we propose a new method for making cancelable fingerprint templates that do not require alignment. For each minutia, a rotation and translation invariant value is computed from the orientation information of neighboring local regions around the minutia. The invariant value is used as the input to two changing functions that output two values for the translational and rotational movements of the original minutia, respectively, in the cancelable template. When a template is compromised, it is replaced by a new one generated by different changing functions. Our approach preserves the original geometric relationships (translation and rotation) between the enrolled and query templates after they are transformed. Therefore, the transformed templates can be used to verify a person without requiring alignment of the input fingerprint images. In our experiments, we evaluated the proposed method in terms of two criteria: performance and changeability. When evaluating the performance, we examined how verification accuracy varied as the transformed templates were used for matching. When evaluating the changeability, we measured the dissimilarities between the original and transformed templates, and between two differently transformed templates, which were obtained from the same original fingerprint. The experimental results show that the two criteria mutually affect each other and can be controlled by varying the control parameters of the changing functions.

Benchmarking a reduced multivariate polynomial pattern classifier

A novel method using a reduced multivariate polynomial model has been developed for biometric decision fusion where simplicity and ease of use could be a concern. However, much to our surprise, the reduced model was found to have good classification accuracy for several commonly used data sets from the Web. In this paper, we extend the single output model to a multiple outputs model to handle multiple class problems. The method is particularly suitable for problems with small number of features and large number of examples. The basic component of this polynomial model boils down to construction of new pattern features which are sums of the original features and combination of these new and original features using power and product terms. A linear regularized least-squares predictor is then built using these constructed features. The number of constructed feature terms varies linearly with the order of the polynomial, instead of having a power law in the case of full multivariate polynomials. The method is simple as it amounts to only a few lines of Matlab code. We perform extensive experiments on this reduced model using 42 data sets. Our results compared remarkably well with best reported results of several commonly used algorithms from the literature. Both the classification accuracy and efficiency aspects are reported for this reduced model.

Exploiting global and local decisions for multimodal biometrics verification

In this paper, we address the multimodal biometric decision fusion problem. By exploring into the user-specific approach for learning and threshold setting, four possible paradigms for learning and decision making are investigated. Since each user requires a decision hyperplane specific to him in order to achieve good verification accuracy, those tedious iterative training methods like the neural network approach would not be suitable. We propose to use a model that requires only a single training step for this application. The four global and local learning and decision paradigms are then explored to observe their decision capability. Besides the proposal of a relevant receiver operating characteristic performance for the local decision, extensive experiments were conducted to observe the verification performance for fusion of two and three biometrics.

Fingerprint image quality analysis

This paper discusses methods in evaluating fingerprint image quality on a local level. Feature vectors covering directional strength, sinusoidal local ridge/valley pattern, ridge/valley uniformity and core occurrences are first extracted from fingerprint image subblocks. Each subblock is then assigned a quality level through pattern classification. Three different classifiers are employed to compare each of its different effectiveness. Positive results have been obtained based on our database.

Deterministic neural classification

This letter presents a minimum classification error learning formulation for a single-layer feedforward network (SLFN). By approximating the nonlinear counting step function using a quadratic function, the classification error rate is shown to be deterministically solvable. Essentially the derived solution is related to an existing weighted least-squares method with class-specific weights set according to the size of data set. By considering the class-specific weights as adjustable parameters, the learning formulation extends the classification robustness of the SLFN without sacrificing its intrinsic advantage of being a closed-form algorithm. While the method is applicable to other linear formulations, our empirical results indicate SLFNs effectiveness on classification generalization.

2^N discretisation of biophasor in cancellable biometrics

BioPhasor was introduced as a form of cancellable biometrics which integrates a set of user-specific random numbers (RN) with biometric features. This BioPhasor was shown to fulfil diversity, reusability and performance requirements in cancellable biometrics formulation. In this paper, we reformulate and enhance the BioPhasor in terms of verification performance and security, through a 2N stage discretisation process. The formulation is experimented under two scenarios (legitimate and stolen RN) using 2400 FERET face images. Apart from the experiments, desired properties such as one-way transformation and diversity are also examined.

Biometric scores fusion based on total error rate minimization

This paper addresses the biometric scores fusion problem from the error rate minimization point of view. Comparing to the conventional approach which treats fusion classifier design and performance evaluation as a two-stage process, this work directly optimizes the target performance with respect to fusion classifier design. Based on a smooth approximation to the total error rate of identity verification, a deterministic solution is proposed to solve the fusion optimization problem. The proposed method is applied to a face and iris verification fusion problem addressing the demand for high security in the modern networked society. Our empirical evaluations show promising potential in terms of decision accuracy and computing efficiency.

Weighted Online Sequential Extreme Learning Machine for Class Imbalance Learning

Most of the existing sequential learning methods for class imbalance learn data in chunks. In this paper, we propose a weighted online sequential extreme learning machine (WOS-ELM) algorithm for class imbalance learning (CIL). WOS-ELM is a general online learning method that alleviates the class imbalance problem in both chunk-by-chunk and one-by-one learning. One of the new features of WOS-ELM is that an appropriate weight setting for CIL is selected in a computationally efficient manner. In one-by-one learning of WOS-ELM, a new sample can update the classification model without waiting for a chunk to be completed. Extensive empirical evaluations on 15 imbalanced datasets show that WOS-ELM obtains comparable or better classification performance than competing methods. The computational time of WOS-ELM is also found to be lower than that of the competing CIL methods.

Model-guided deformable hand shape recognition without positioning aids

This work addresses the problem of deformable hand shape recognition in biometric systems without any positioning aids. We separate and recognize multiple rigid fingers under Euclidean transformations. An elliptical model is introduced to represent fingers and accelerate the search of optimal alignments of fingers. Unlike other methods, the similarity is measured during alignment search based on finger width measurements defined at nodes by controllable intervals to achieve balanceable recognition accuracy and computational cost. Technically, our method bridges the traditional handcrafted-feature methods and the shape-distance methods. We have tested it using our 108-person-540-sample database with significantly increased positive recognition accuracy.

A reduced multivariate polynomial model for multimodal biometrics and classifiers fusion

The multivariate polynomial model provides an effective way to describe complex nonlinear input-output relationships since it is tractable for optimization, sensitivity analysis, and prediction of confidence intervals. However, for high-dimensional and high-order problems, multivariate polynomial regression becomes impractical due to its huge number of product terms. This is especially true for the case of a full interaction model. In this paper, we propose a reduced multivariate polynomial model to circumvent the dimensionality problem with some compromise in its approximation capability. In multimodal biometrics and many classifiers fusion applications, as individual classifiers to be combined would have attained a certain level of classification accuracy, this reduced multivariate polynomial model can be used to combine these classifiers in the next level of classification taking their outputs as the inputs to the reduced multivariate polynomial model. The model is first applied to a well-known pattern classification problem to illustrate its classification capability. The reduced multivariate polynomial model is then applied to combine two biometric verification systems with improved receiver operating characteristics performance as compared to an optimal weighing method and a few commonly used classifiers.