Multi-bits biometric string generation based on the likelyhood ratio

Preserving the privacy of biometric information stored in biometric systems is becoming a key issue. An important element in privacy protecting biometric systems is the quantizer which transforms a normal biometric template into a binary string. In this paper, we present a user-specific quantization method based on a likelihood ratio approach (LQ). The bits generated from every feature are concatenated to form a fixed length binary string that can be hashed to protect its privacy. Experiments are carried out on both fingerprint data (FVC2000) and face data (FRGC). Results show that our proposed quantization method achieves a reasonably good performance in terms of FAR/FRR (when FAR is 10−4, the corresponding FRR are 16.7% and 5.77% for FVC2000 and FRGC, respectively)

Fingerprint Verification Using Spectral Minutiae Representations

Most fingerprint recognition systems are based on the use of a minutiae set, which is an unordered collection of minutiae locations and orientations suffering from various deformations such as translation, rotation, and scaling. The spectral minutiae representation introduced in this paper is a novel method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require a fixed-length feature vector. This paper introduces the concept of algorithms for two representation methods: the location-based spectral minutiae representation and the orientation-based spectral minutiae representation. Both algorithms are evaluated using two correlation-based spectral minutiae matching algorithms. We present the performance of our algorithms on three fingerprint databases. We also show how the performance can be improved by using a fusion scheme and singular points

A Fast Minutiae-Based Fingerprint Recognition System

The spectral minutiae representation is a method to represent a minutiae set as a fixed-length feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. These characteristics enable the combination of fingerprint recognition systems with template protection schemes that require as an input a fixed-length feature vector. Based on the spectral minutiae features, this paper introduces two feature reduction algorithms: the Column Principal Component Analysis and the Line Discrete Fourier Transform feature reductions, which can efficiently compress the template size with a reduction rate of 94%. With reduced features, we can also achieve a fast minutiae-based matching algorithm. This paper presents the performance of the spectral minutiae fingerprint recognition system and shows a matching speed with 125 000 comparisons per second on a PC with Intel Pentium D processor 2.80 GHz and 1 GB of RAM. This fast operation renders our system suitable as a preselector for a large-scale fingerprint identification system, thus significantly reducing the time to perform matching, especially in systems operating at geographical level (e.g., police patrolling) or in complex critical environments (e.g., airports)

Error reduction in two-dimensional pulse-area modulation, with application to computer-generated tranparencies

The paper deals with the analysis of computer-generated half-tone transparencies that are realised as a regular array of area-modulated unit-height pulses and with the help of which we want to generate [via low-pass filtering] band-limited space functions by optical means. The mathematical basis for such transparencies is, of course, the well-known sampling theorem [1], which says that a band-limited func-tion y(x), say, [with x a two-dimensional spatial column vector] can be generated by properly low-pass filtering a regular array of Dirac functions whose weights are proportional to the required sample values yey(Xm) [with X the sampling matrix and m=(mi,m2)t an integer-valued column vector; the superscript t denotes transposition]

Practical Biometric Authentication with Template Protection

We show the feasibility of template protecting biometric authentication systems. In particular, we apply template protection schemes to fingerprint data. Therefore we first make a fixed length representation of the fingerprint data by applying Gabor filtering. Next we introduce the reliable components scheme. In order to make a binary representation of the fingerprint images we extract and then quantize during the enrollment phase the reliable components with the highest signal to noise ratio. Finally, error correction coding is applied to the binary representation. It is shown that the scheme achieves an EER of approximately 4.2% with secret length of 40 bits in experiments

Information-theoretic security analysis of physical uncloneable functions

We propose a general theoretical framework to analyze the security of Physical Uncloneable Functions (PUFs). We apply the framework to optical PUFs. In particular we present a derivation, based on the physics governing multiple scattering processes, of the number of independent challenge-response pairs supported by a PUF. We find that the number of independent challenge-response pairs is proportional to the square of the thickness of the PUF and inversely proportional to the scattering length and the wavelength of the laser light. We compare our results to those of Pappu and show that they coincide in the case where the density of scatterers becomes very high.Finally, we discuss some attacks on PUFs, and introduce the Slow PUF as a way to thwart brute force attacks