Fingerprint identification is based on two basic premises: (i) persistence: the basic characteristics of fingerprints do not change with time; and (ii) individuality: the fingerprint is unique to an individual. The validity of the first premise has been established by the anatomy and morphogenesis of friction ridge skin. While the second premise has been generally accepted to be true based on empirical results, the underlying scientific basis of fingerprint individuality has not been formally tested. As a result, fingerprint evidence is now being challenged in several court cases. We address the problem of fingerprint individuality by quantifying the amount of information available in minutiae points to establish a correspondence between two fingerprint images. We derive an expression which estimates the probability of falsely associating minutiae-based representations from two arbitrary fingerprints. For example, the probability that a fingerprint with 36 minutiae points will share 15 minutiae points with another arbitrarily chosen fingerprint with 36 minutiae points is 4:26 10 7 . These probability estimates are compared with typical fingerprint matcher accuracy results. Our results show that (i) contrary to the popular belief fingerprint matching is not infallible and leads to some false associations, (ii) the performance of automatic fingerprint matcher does not even come close to the theoretical performance, and (iii) due to the limited information content of the minutiae-based representation, the automatic system designers should explore the use of non-minutiaebased information present in the fingerprints. 1

With an increasing emphasis on security, automated personal identification based on biometrics has been receiving extensive attention over the past decade. Iris recognition, as an emerging biometric recognition approach, is becoming a very active topic in both research and practical applications. In general, a typical iris recognition system includes iris imaging, iris liveness detection, and recognition. This paper focuses on the last issue and describes a new scheme for iris recognition from an image sequence. We first assess the quality of each image in the input sequence and select a clear iris image from such a sequence for subsequent recognition. A bank of spatial filters, whose kernels are suitable for iris recognition, is then used to capture local characteristics of the iris so as to produce discriminating texture features. Experimental results show that the proposed method has an encouraging performance. In particular, a comparative study of existing methods for iris recognition is conducted on an iris image database including 2,255 sequences from 213 subjects. Conclusions based on such a comparison using a nonparametric statistical method (the bootstrap) provide useful information for further research.

The latest research indicates using a combination of biometric avenues for human identification is more effective, and far more challenging.

Most fingerprint matching systems rely on the distribution of minutiae on the fingertip to represent and match fingerprints. While the ridge flow pattern is generally used for classifying fingerprints, it is seldom used for matching. This paper describes a hybrid fingerprint matching scheme that uses both minutiae and ridge flow information to represent and match fingerprints. A set of 8 Gabor filters, whose spatial frequencies correspond to the average inter-ridge spacing in fingerprints, is used to capture the ridge strength at equally spaced orientations. A square tessellation of the filtered images is then used to construct an eight-dimensional feature map, called the ridge feature map. The ridge feature map along with the minutiae set of a fingerprint image is used for matching purposes. The proposed technique has the following features: (i) the entire image is taken into account while constructing the ridge feature map; (ii) minutiae matching is used to determine the translation and rotation parameters relating the query and the template images for ridge feature map extraction; (iii) filtering and ridge feature map extraction are implemented in the frequency domain thereby speeding up the matching process; (iv) filtered query images are cached to greatly increase the one-to-many matching speed. The hybrid matcher performs better than a minutiae-based fingerprint matching system. The genuine accept rate of the hybrid matcher is observed to be 10% higher than that of a minutiae-based system at low FAR values. Fingerprint verification (one-to-one matching) using the hybrid matcher on a Pentium III, 800 MHz system takes 1.4 seconds, while fingerprint identification (one-to-many matching) involving 1, 000 templates takes 0.2 seconds per match.

A scheme is proposed for classifier combination at decision level which stresses the importance of classier selection during combination. The proposed scheme is optimal (in the Neyman-Pearson sense) when sufficient data are available to obtain reasonable estimates of the join densities of classifier outputs. Four different fingerprint matching algorithms are combined using the proposed scheme to improve the accuracy of a fingerprint verification system. Experiments conducted on a large fingerprint database ( 2,700 ngerprints) conrm the effectiveness of the proposed integration scheme. An overall matching performance increase of 3% is achieved. We further show that a combination of multiple impressions or multiple fingers improves the verification performance by more than 4% and 5%, respectively. Analysis of the results provide some insight into the various decision-level classifier combination strategies.

For the alignment of two fingerprints certain landmark points are needed. These should be automaticly extracted with low misidentification rate. As landmarks we suggest the prominent symmetry points (singular points, SPs) in the fingerprints. We identify an SP by its symmetry properties. SPs are extracted from the complex orientation field estimated from the global structure of the fingerprint, i.e. the overall pattern of the ridges and valleys. Complex filters, applied to the orientation field in multiple resolution scales, are used to detect the symmetry and the type of symmetry. Experimental results are reported.

Raw image data offer rich source of information for matching and classification. For simplicity of pattern recognition system design, a sequential approach consisting of sensing, feature extraction and matching is conventionally adopted where each stage transforms a particular component of information relatively independently. The interaction between these modules is limited. Some of the errors in the end-to-end sequential processing can be easily eliminated especially for the feature extraction stage by revisiting the original image data. We propose a feedback path for the feature extraction stage, followed by a feature refinement stage for improving the matching performance. This performance improvement is illustrated in the context of a minutiae-based fingerprint verification system. We show that a minutia verification stage based on reexamining the gray-scale profile in a detected minutia's spatial neighborhood in the sensed image can improve the matching performance by ~4% on our database. Further, we show that a feature refinement stage which assigns a class label to each detected minutia (ridge ending and ridge bifurcation) before matching can also improve the matching performance by ~3%. A combination of feedback (minutia verification) in the feature extraction phase and feature refinement (minutia classification) improves the overall performance of the fingerprint verification system by ~8%.

Introduction More than a century has passed since Alphonse Bertillon first conceived and then industriously practiced the idea of using body measurements for solving crimes [1]. Just as his idea was gaining popularity, it faded into relative obscurity by a far more significant and practical discovery of the uniqueness of the human fingerprints 1 . Soon after this discovery, many major law enforcement departments embraced the idea of first "booking" the fingerprints of criminals, so that their records are readily available and later using leftover fingerprint smudges (latents), the identity of criminals can be determined. These agencies sponsored a rigorous study of fingerprints, developed scientific 1 In 1893, the Home Ministry Office, UK, accepted that no two individuals have the same fingerprints. 1 methods for visual matching of fingerprints and strong programs/cultures for training fingerprint experts, and applied the art

We present a novel fingerprint alignment and matching scheme that utilizes ridge feature maps to represent, align and match fingerprint images. The technique described here obviates the need for extracting minutiae points or the core point to either align or match fingerprint images.

Fingerprints are widely used in automatic identity verification systems. The core of such systems is the verification algorithm to match two fingerprints. So far, various method for fingerprint matching have been proposed, but few works investigated the fusion of two or more matching algorithms. In this paper, various methods for fusing such algorithms have been investigated. Experimental results showed that such fusion can outperform the best individual verification algorithm and increase the discrimination between genuine and impostor classes.