Showing papers by "Nalini K. Ratha published in 2000"

Method for biometric-based authentication in wireless communication for access control

Abstract: Smart cards systems that are utilized in biometric authentication are slow in processing and have the cards themselves have the added disadvantage of being misplaced or lost. Moreover, storing biometric data (on a database) over a network poses security issues that in extreme instances can be compromised. Significant security can be achieved if the biometric templates are stored locally in a portable device. A user can use the portable device to either transmit wirelessly the stored biometric for authentication purposes, or a user can locally measure a biometric using the portable device and match it against a biometric which is also stored locally (in the portable device).

Robust fingerprint authentication using local structural similarity

Abstract: Fingerprint matching is challenging as the matcher has to minimize two competing error rates: the False Accept Rate and the False Reject Rate. We propose a novel, efficient, accurate and distortion-tolerant fingerprint authentication technique based on graph representation. Using the fingerprint minutiae features, a labeled, and weighted graph of minutiae is constructed for both the query fingerprint and the reference fingerprint. In the first phase, we obtain a minimum set of matched node pairs by matching their neighborhood structures. In the second phase, we include more pairs in the match by comparing distances with respect to matched pairs obtained in first phase. An optional third phase, extending the neighborhood around each feature, is entered if we cannot arrive at a decision based on the analysis in first two phases. The proposed algorithm has been tested with excellent results on a large private livescan database obtained with optical scanners.

System and method for distorting a biometric for transactions with enhanced security and privacy

Abstract: Authentication methods are very important in several applications. Existing methods of authentication based on possessions or knowledge have several problems that can be overcome by using biometrics. Unfortunately biometrics-based authentication is unrevocable today and has many privacy concerns in users' minds. The proposed technique employs signal scrambling and morphing techniques to intentionally distort the original biometrics signal in a non-invertible fashion. If the security is compromised, the system can cancel a particular distortion and reacquire the signal with a new distortion function. This provides functionality as good as non-biometric authentication methods in terms of their power of revocation.

Secure data hiding in wavelet compressed fingerprint images

Abstract: With the rapid growth of the Internet, electronic commerce revenue now amounts to several billion US dollars. To avoid fraud and misuse, buyers and sellers desire more secure methods of authentication than today's userid and password combinations. Automated biometrics technology in general, and fingerprints in particular, provide an accurate and reliable authentication method. However, fingerprint-based authentication requires accessing fingerprint images scanned remotely at the user's workstation, a potentially weak point in the security system. Stored or synthetic fingerprint images might be fraudulently transmitted, even if the communication channel itself is encrypted. In this paper we describe an algorithm for secure data hiding in wavelet compressed fingerprint images to alleviate this problem. Assuming the image capture device is secure, then only the decompressor on the server can locate the embedded message and thereby validate the submitted image.

Evaluation techniques for biometrics-based authentication systems (FRR)

Abstract: Biometrics-based authentication is becoming popular because of increasing ease-of-use and reliability. Performance evaluation of such systems is an important issue. We attempt to address two aspects of performance evaluation that have been conventionally neglected. First, the "difficulty" of the data that is used in a study influences the evaluation results. We propose some measures to characterize the data set so that the performance of a given system on different data sets can be compared. Second, conventional studies often have reported the false reject and false accept rates (FRR, FAR) in the form of match score distributions. However, no confidence intervals are computed for these distributions, hence no indication of the significance of the estimates is given. In this paper, we systematically study and compare parametric and nonparametric (bootstrap) methods for measuring confidence intervals. We give special attention to false reject rate estimates.

Detecting dynamic behavior in compressed fingerprint videos: distortion

Abstract: Distortions in fingerprint images arising from the elasticity of finger skin and the pressure and movement of fingers during image capture lead to great difficulties in establishing a match between multiple images acquired from a single finger. In a single fingerprint image depicting a finger at some given instant of time, it is difficult to get any distortion information. Further, static two-dimensional or three-dimensional (electronic) copies of fingerprints can be fabricated and used to spoof remote biometric security systems since the input required by the systems is not a function of time. This paper addresses these issues, by proposing the novel use of fingerprint video sequences to investigate and exploit dynamic behaviors manifested by fingers over time during image acquisition. In particular, we present a novel approach to detect and estimate distortion occurring in fingerprint video streams. Our approach directly works on MPEG-{1, 2} encoded fingerprint video bitstreams to estimate interfield flow without decompression, and uses flow characteristics to investigate temporal behaviour of the fingerprints. The joint temporal and motion analysis leads to a novel technique to detect and characterize distortion reliably. The proposed method has been tested on the NIST 24 database and the results are very promising.

FPGA-based coprocessor for text string extraction

Abstract: In document understanding, one of the early stages involves extracting text strings from a scanned image of the document. Often, the text is printed on a repetitive background of design patterns for visual effects. For recognition purposes, the text strings need to be extracted eliminating the background. Image morphology based algorithms have been proposed for this purpose. However, image morphology operations are compute intensive. We describe the design and synthesis of a high-performance coprocessor to meet the compute load. The algorithm has been synthesized for Splash 2, an attached processor on Sun hosts. The Xilinx Field-Programmable Gate Array (FPGA) based PEs are programmed using VHDL behavioral modeling. The design can run at near-ASIC speeds of /spl ap/22 MHz clock rate with effective timing of 3 milliseconds per 128/spl times/128 image frame and 3/spl times/3 structuring element. Compared with a SPARC station 20 timings of 1.5 sees, the present implementation has a speed advantage of the order of 500 times.

Learning partitioned least squares filters for fingerprint enhancement

Abstract: Fingerprint images contain varying amount of noise because of the limitations of the fingerprint acquisition process. It is often necessary to enhance such noisy fingerprint images so that the features extracted from them are reliable. We propose a novel approach to fingerprint enhancement where a set of filters are learned using the "learn-from-example" paradigm. An expert provides the ground truth information for ridges in a small set of representative fingerprint images. The space of local fingerprint patterns in a small neighborhood is partitioned into a set of expressive yet computationally simple classes. A filter is learnt for each partition by finding the optimal linear mapping (in least-square sense) from the input to the enhanced space. The proposed approach offers distinct performance and speed advantages for a wide variety of fingerprint images.

Hierarchical partitioned least squares filter-bank for fingerprint enhancement

Abstract: It is desirable to enhance the fingerprint image for achieving good fingerprint matching performance. In our earlier work, we have proposed a method for learning a set of partitioned least-squares filters from a given set of images and ground truth pairs. In this paper, we propose a refined approach to learn a class of hierarchical filter banks to extend our enhancement technique. The new technique is shown to obtain much better results particularly when the input image is not easily restored using our earlier technique. We evaluate the performance of our new approach to fingerprint enhancement filter design by assessing the effect of enhancement on performance of (i) fingerprint feature extraction and (ii) fingerprint matching.