Wei-Yun Yau

Fingerprint minutiae matching based on the local and global structures

Proposes a fingerprint minutia matching technique, which matches the fingerprint minutiae by using both the local and global structures of minutiae. The local structure of a minutia describes a rotation and translation invariant feature of the minutia in its neighborhood. It is used to find the correspondence of two minutiae sets and increase the reliability of the global matching. The global structure of minutiae reliably determines the uniqueness of fingerprint. Therefore, the local and global structures of minutiae together provide a solid basis for reliable and robust minutiae matching. The proposed minutiae matching scheme is suitable for an online processing due to its high processing speed. Experimental results show the performance of the proposed technique.

Fingerprint quality and validity analysis

Discusses methods to estimate the quality as well as validity of a fingerprint image. Orientation certainty is used to certify the localized texture pattern of the fingerprint images while ridge to valley structure is analyzed to detect invalid images. Global uniformity and continuity ensures that the image is valid as a whole. 150 images with various qualities are evaluated using the proposed algorithm and quality benchmark we defined. A monotonic relationship is found indicating that the proposed algorithm is feasible in detecting low quality as well as invalid fingerprint images.

Detecting the fingerprint minutiae by adaptive tracing the gray-level ridge

Abstract This paper presents a minutiae detection procedure based on adaptive tracing the gray-level ridge of the fingerprint image with piecewise linear lines of different length. The original fingerprint image is smoothed with an adaptive-oriented smoothing filter only at some selected points. This will greatly reduce the computational time. Each ridge in the skeleton is labeled with a number so that each detected minutia is associated with one or two ridge numbers, which is useful for post processing. We objectively assess the performance of this approach by using two large fingerprint databases.

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.

Boosting dense SIFT descriptors and shape contexts of face images for gender recognition

In this paper, we propose a novel face representation in which a face is represented in terms of dense Scale Invariant Feature Transform (d-SIFT) and shape contexts of the face image. The application of the representation in gender recognition has been investigated. There are four problems when applying the SIFT to facial gender recognition. (1) There may be only a few keypoints that can be found in a face image due to the missing texture and poorly illuminated faces; (2) The SIFT descriptors at the keypoints (we called it sparse SIFT) are distinctive whereas alternative descriptors at non-keypoints (e.g. grid) could cause negative impact on the accuracy; (3) Relatively larger image size is required to obtain sufficient keypoints support the matching and (4) The matching assumes that the faces are properly registered. This paper addresses these difficulties using a combination of SIFT descriptors and shape contexts of face images. Instead of extracting descriptors around interest points only, local feature descriptors are extracted at regular image grid points that allow for a dense description of the face images. In addition, the global shape contexts of the face images are fused with the dense SIFT to improve the accuracy. AdaBoost is adopted to select features and form a strong classifier. The proposed approach is then applied to solve the problem of gender recognition. The experimental results on a large set of faces showed that the proposed method can achieve high accuracies even for faces that are not aligned.

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.

Combination of hyperbolic functions for multimodal biometrics data fusion

In this paper, we treat the problem of combining fingerprint and speech biometric decisions as a classifier fusion problem. By exploiting the specialist capabilities of each classifier, a combined classifier may yield results which would not be possible in a single classifier. The feedforward neural network provides a natural choice for such data fusion as it has been shown to be a universal approximator. However, the training process remains much to be a trial-and-error effort since no learning algorithm can guarantee convergence to optimal solution within finite iterations. In this work, we propose a network model to generate different combinations of the hyperbolic functions to achieve some approximation and classification properties. This is to circumvent the iterative training problem as seen in neural network learning. In many decision data fusion applications, since individual classifiers or estimators to be combined would have attained a certain level of classification or approximation accuracy, this hyperbolic function network can be used to combine these classifiers taking their decision outputs as the inputs to the network. The proposed hyperbolic function network model is first applied to a function approximation problem to illustrate its approximation capability. This is followed by some case studies on pattern classification problems. The model is finally applied to combine the fingerprint and speaker verification decisions which show either better or comparable results with respect to several commonly used methods.

Fingerprint and speaker verification decisions fusion using a functional link network

By exploiting the specialist capabilities of each classifier, a combined classifier may yield results which would not be possible with a single classifier. In this paper, we propose to combine the fingerprint and speaker verification decisions using a functional link network. This is to circumvent the nontrivial trial-and-error and iterative training effort as seen in backpropagation neural networks which cannot guarantee global optimal solutions. In many data fusion applications, as individual classifiers to be combined would have attained a certain level of classification accuracy, the proposed functional link network can be used to combine these classifiers by taking their outputs as the inputs to the network. The proposed network is first applied to a pattern recognition problem to illustrate its approximation capability. The network is then used to combine the fingerprint and speaker verification decisions with much improved receiver operating characteristics performance as compared to several decision fusion methods from the literature.