Chulhan Lee

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.

Cancelable fingerprint templates using minutiae-based bit-strings

It has become critical to protect biometric templates in the current biometric community. One way for doing this is using a cancelable biometric method, which transforms original biometric templates in a non-invertible way and uses those transformed templates to verify a persons identity. In this paper, we propose a new method to generate cancelable bit-strings (templates) from fingerprint minutiae. Our method is to provide a simple mean to generate cancelable templates without requiring for pre-alignment of fingerprints. The main idea is to map the minutiae into a predefined 3 dimensional array which consist of small cells and find out which cells include minutiae. To do this, we choose one of minutiae as a reference minutia and other minutiae are translated and rotated in order to map the minutiae into the cells based on the position and orientation of the reference minutia. After mapping, we set the cells in the 3D array to 1 if they include more than one minutia otherwise the cells are set to 0. A 1D bit-string is generated by sequentially visiting the cells in the 3D array. The order of the 1D bit-string is permuted according to the type of reference minutiae and users PIN so that we can regenerate new templates when we need them. Finally, cancelable bit-strings are generated by changing the reference minutia into another minutia in turn. In the experiments, we evaluate our method using the FVC2004 database and show that the performance is better than that of a previous method.

A new scheme for touchless fingerprint recognition system

Fingerprint-based recognition systems are widely used in the field of biometrics. Most of the fingerprint sensors developed so far acquire fingerprint images through the surface of a solid plate, which degrades the recognition performance due to deformations. These deformations are caused by the pressure of the physical contact that is costly and hard to estimate. A touchless fingerprint recognition system is devised using a camera sensor to resolve this problem. However, this system raises some new problems, such as defocusing, low ridge-valley contrast, 3D-to-2D mapping, and so forth. Adequate solutions are discussed by introducing modified steps to the algorithm. Our proposed system is compared with a primitive touchless sensor with no manipulation and the algorithm of the preceding touch-based system. Future work is suggested.

A study of touchless fingerprint recognition system

Fingerprint recognition systems are widely used in the field of biometrics. Many existing fingerprint sensors acquire fingerprint images as the user’s fingerprint is contacted on a solid flat sensor. Because of this contact, input images from the same finger can be quite different and there are latent fingerprint issues that can lead to forgery and hygienic problems. For these reasons, a touchless fingerprint recognition system has been investigated, in which a fingerprint image can be captured without contact. While this system can solve the problems which arise through contact of the user’s finger, other challenges emerge, for example, low ridge-valley contrast, and 3D to 2D image mapping. In this paper we discuss both the disadvantages and the advantages of touchless fingerprint systems and introduce the hardware and algorithm approach to solve the problems. We describe the structure of illuminator and the wavelength of light to acquire a high contrast fingerprint images. To solve the problem of 3D to 2D image mapping, we describe the method to remove the strong view difference fingerprint images. Experiments show that the touchless fingerprint system has better performance than the conventional touch based system.

Changeable Biometrics for Appearance Based Face Recognition

To enhance security and privacy in biometrics, changeable (or cancelable) biometrics have recently been introduced. The idea is to transform a biometric signal or feature into a new one for enrollment and matching. In this paper, we proposed changeable biometrics for face recognition using an appearance based approach. PCA and ICA coefficient vectors extracted from an input face image are normalized using their norm. The two normalized vectors are scrambled randomly and a new transformed face coefficient vector (transformed template) is generated by addition of the two normalized vectors. When a transformed template is compromised, it is replaced by using a new scrambling rule. Because the transformed template is generated by the addition of two vectors, the original PCA and ICA coefficients cannot be recovered from the transformed coefficients. In our experiment, we compared the performance between the cases when PCA and ICA coefficient vectors are used for verification and when the transformed coefficient vectors are used for verification.

Model-based quality estimation of fingerprint images

Most automatic fingerprint identification systems identify a person using minutiae. However, minutiae depend almost entirely on the quality of the fingerprint images that are captured. Therefore, it is important that the matching step uses only reliable minutiae. The quality estimation algorithm deduces the availability of the extracted minutiae and allows for a matching step that will use only reliable minutiae. We propose a model-based quality estimation of fingerprint images. We assume that the ideal structure of a fingerprint image takes the shape of a sinusoidal wave consisting of ridges and valleys. To determine the quality of a fingerprint image, the similarity between the sinusoidal wave and the input fingerprint image is measured. The proposed method uses the 1-dimensional (1D) probability density function (PDF) obtained by projecting the 2-dimensional (2D) gradient vectors of the ridges and valleys in the orthogonal direction to the local ridge orientation. Quality measurement is then caculated as the similarity between the 1D probability density functions of the sinusoidal wave and the input fingerprint image. In our experiments, we compared the proposed method and other conventional methods using FVC-2002 DB I, III procedures. The performance of verification and the separability between good and bad regions were tested.

Feature extraction using the Bhattacharyya distance

The Bhattacharyya distance provides valuable information in determining the effectiveness of a feature set and has been used as a separability measure for feature selection. In Lee (1997), it has been shown that it is feasible to predict the classification error accurately using the Bhattacharyya distance. The new formula makes it possible to estimate classification error between two classes within 1-2% margin. In this paper, we propose a new feature extraction method utilizing the result. Initially, we start with an arbitrary feature vector. Assuming that the feature vector is used for classification, we estimate the classification error using the error estimation formula. Then we move the feature vector slightly in the direction so that the estimated classification error is decreased most rapidly. This can be done by taking a gradient. Experiments show that the proposed method compares favorably with the conventional methods.

Performance Based Revocable Biometrics

We show that revocable biometrics can be performance driven in this paper. The main idea is to optimize the verification performance objective directly from a transformation projection for revocable or cancelable biometric applications. Through an error rate cost function, an optimally transformed face template can be obtained for identity concealment. Our preliminary experiments validated the feasibility.

A Changeable Biometric System That Uses Parts-Based Localized Representation for Face Recognition

Biometric data cannot be changed or canceled if they are compromised. To cope with this problem, changeable biometric systems that use transformed biometric data instead of original data have recently been introduced. In this paper, we propose a changeable biometric system for face recognition that uses LNMF (local non-negative matrix factorization), or parts-based localized representation. Two different sets of LNMF bases can be computed from given training images when training them twice and two different LNMF feature vectors can then be extracted from an input face image using these LNMF bases. The two feature vectors are scrambled randomly and a new transformed feature vector can be generated by the addition of the two feature vectors. The scrambling rule is determined by a given users PIN, and when the transformed feature vector is compromised, it is replaced by using a new scrambling rule. Because the transformed template is generated by the addition of two vectors, the two different original LNMF feature vectors cannot be recovered from the transformed feature vector. Experimental results show that the proposed method performs better than the PCA and original LNMF-based methods. Also the transformed feature vector satisfies the requirement of changeability.