Likelihood-Ratio-Based Biometric Verification

The paper presents results on optimal similarity measures for biometric verification based on fixed-length feature vectors. First, we show that the verification of a single user is equivalent to the detection problem, which implies that, for single-user verification, the likelihood ratio is optimal. Second, we show that, under some general conditions, decisions based on posterior probabilities and likelihood ratios are equivalent and result in the same receiver operating curve. However, in a multi-user situation, these two methods lead to different average error rates. As a third result, we prove theoretically that, for multi-user verification, the use of the likelihood ratio is optimal in terms of average error rates. The superiority of this method is illustrated by experiments in fingerprint verification. It is shown that error rates below 10/sup -3/ can be achieved when using multiple fingerprints for template construction

Grip-Pattern Recognition for Smart Guns

This paper describes the design, implementation and evaluation of a user-verification system for a smart gun, which is based on grip-pattern recognition. An existing pressure sensor consisting of an array of 44 x 44 piezoresistive elements has been used. An interface has been developed to acquire pressure images from the sensor. The values of the pixels in the pressure-pattern images are used as inputs for a verification algorithm, which is currently implemented in software on a computer. The verification algorithm is based on a likelihood-ratio classifier for Gaussian probability densities. First results indicate that it is possible to use grip-pattern recognition for biometric verification, when allowing a certain false-rejection and false-acceptance rate. However, more measurements are needed to give a more reliable indication of the systems performance

Multimodal Personal Verification Using Likelihood Ratio for the Match Score Fusion

In this paper, the authors present a novel personal verification system based on the likelihood ratio test for fusion of match scores from multiple biometric matchers (face, fingerprint, hand shape, and palm print). In the proposed system, multimodal features are extracted by Zernike Moment (ZM). After matching, the match scores from multiple biometric matchers are fused based on the likelihood ratio test. A finite Gaussian mixture model (GMM) is used for estimating the genuine and impostor densities of match scores for personal verification. Our approach is also compared to some different famous approaches such as the support vector machine and the sum rule with min-max. The experimental results have confirmed that the proposed system can achieve excellent identification performance for its higher level in accuracy than different famous approaches and thus can be utilized for more application related to person verification

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)

Biometric Authentication System on Mobile Personal Devices

We propose a secure, robust, and low-cost biometric authentication system on the mobile personal device for the personal network. The system consists of the following five key modules: 1) face detection; 2) face registration; 3) illumination normalization; 4) face verification; and 5) information fusion. For the complicated face authentication task on the devices with limited resources, the emphasis is largely on the reliability and applicability of the system. Both theoretical and practical considerations are taken. The final system is able to achieve an equal error rate of 2% under challenging testing protocols. The low hardware and software cost makes the system well adaptable to a large range of security applications

Forensic Face Recognition: A Survey

Beside a few papers which focus on the forensic aspects of automatic face recognition, there is not much published about it in contrast to the literature on developing new techniques and methodologies for biometric face recognition. In this report, we review forensic facial identification which is the forensic experts‟ way of manual facial comparison. Then we review famous works in the domain of forensic face recognition. Some of these papers describe general trends in forensics [1], guidelines for manual forensic facial comparison and training of face examiners who will be required to verify the outcome of automatic forensic face recognition system [2]. Some proposes theoretical framework for application of face recognition technology in forensics [3] and automatic forensic facial comparison [4, 5]. Bayesian framework is discussed in detail and it is elaborated how it can be adapted to forensic face recognition. Several issues related with court admissibility and reliability of system are also discussed. \ud Until now, there is no operational system available which automatically compare image of a suspect with mugshot database and provide result usable in court. The fact that biometric face recognition can in most cases be used for forensic purpose is true but the issues related to integration of technology with legal system of court still remain to be solved. There is a great need for research which is multi-disciplinary in nature and which will integrate the face recognition technology with existing legal systems. In this report we present a review of the existing literature in this domain and discuss various aspects and requirements for forensic face recognition systems particularly focusing on Bayesian framework

Matching hand radiographs

Biometric verification and identification methods of medical images can be used to find possible inconsistencies in patient records. Such methods may also be useful for forensic research. In this work we present a method for identifying patients by their hand radiographs. We use active appearance model representations presented before [1] to extract 64 shape features per bone from the metacarpals, the proximal, and the middle phalanges. The number of features was reduced to 20 by applying principal component analysis. Subsequently, a likelihood ratio classifier [2] determines whether an image potentially belongs to another patient in the data set. Firstly, to study the symmetry between both hands, we use a likelihood-ratio classifier to match 45 left hand images to a database of 44 (matching) right hand images and vice versa. We found an average equal error probability of 6.4%, which indicates that both hand shapes are highly symmetrical. Therefore, to increase the number of samples per patient, the distinction between left and right hands was omitted. Secondly, we did multiple experiments with randomly selected training images from 24 patients. For several patients there were multiple image pairs available. Test sets were created by using the images of three different patients and 10 other images from patients that were in the training set. We estimated the equal error rate at 0.05%. Our experiments suggest that the shapes of the hand bones contain biometric information that can be used to identify persons

A Comparison of Hand-Geometry Recognition Methods Based on Low- and High-Level Features

This paper compares the performance of hand-geometry recognition based on high-level features and on low-level features. The difference between high- and low-level features is that the former are based on interpreting the biometric data, e.g. by locating a finger and measuring its dimensions, whereas the latter are not. The low-level features used here are landmarks on the contour of the hand. The high-level features are a standard set of geometrical features such as widths and lengths of fingers and angles, measured at preselected locations