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Multi-Bits Biometric String Generation based on the Likelihood Ratio
"... Abstract — 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 q ..."
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Abstract — 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). I.
Biometric Verification Based on Grip-Pattern Recognition
- In Security, Steganography, and Watermarking of Multimedia Contents
, 2004
"... 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 44 piezoresistive elements is used to measure the grip pattern. An interface has been ..."
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Cited by 12 (7 self)
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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 44 piezoresistive elements is used to measure the grip pattern. 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 PC. The verification algorithm is based on a likelihoodratio classifier for Gaussian probability densities. First results indicate that it is feasible to use grip-pattern recognition for biometric verification.
R.M.: Biometric Verification: Looking Beyond Raw Similarity Scores
- In: Workshop on Biometrics (CVPR
, 2006
"... Most biometric verification techniques make decisions based solely on a score that represents the similarity of the query template with the reference template of the claimed identity stored in the database. When multiple templates are available, a fusion scheme can be designed using the similarities ..."
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Most biometric verification techniques make decisions based solely on a score that represents the similarity of the query template with the reference template of the claimed identity stored in the database. When multiple templates are available, a fusion scheme can be designed using the similarities with these templates. Combining several templates to construct a composite template and selecting a set of useful templates has also been reported in addition to usual multi-classifier fusion methods when multiple matchers are available. These commonly adopted techniques rarely make use of the large number of non-matching templates in the database or training set. In this paper, we highlight the usefulness of such a fusion scheme while focusing on the problem of fingerprint verification. For each enrolled template, we identify its cohorts (similar fingerprints) based on a selection criterion. The similarity scores of the query template with the reference template and its cohorts from the database are used to make the final verification decision using two approaches: a likelihood ratio based normalization scheme and a Support Vector Machine (SVM)-based classifier. We demonstrate the accuracy improvements using the proposed method with no a priori knowledge about the database or the matcher under consideration using a publicly available database and matcher. Using our cohort selection procedure and the trained SVM, we show that accuracy can be significantly improved at the expense of few extra matches. 1.
Likelihood Ratio-Based Detection of Facial Features
, 2000
"... One of the first steps in face recognition, after image acquisition, is registration. A simple but effective technique of registration is to align facial features, such as eyes, nose and mouth, as well as possible to a standard face. This requires an accurate automatic estimate of the locations of t ..."
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Cited by 5 (4 self)
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One of the first steps in face recognition, after image acquisition, is registration. A simple but effective technique of registration is to align facial features, such as eyes, nose and mouth, as well as possible to a standard face. This requires an accurate automatic estimate of the locations of those features. This contribution proposes a method for estimating the locations of facial features based on likelihood ratio-based detection. A postprocessing step that evaluates the topology of the facial features is added to reduce the number of false detections. Although the individual detectors only have a reasonable performance (equal error rates range from 3.3% for the eyes to 1.0% for the nose), the positions of the facial features are estimated correctly in 95% of the face images.
Grip-pattern recognition for smart guns
- in Proceedings of ProRISC 2003, 14th Annual Workshop on Circuits, Systems and Signal Processing
, 2003
"... Abstract — 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 × 44 piezoresistive elements has been used. An interface has been developed to ..."
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Abstract — 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 × 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 system’s performance. Keywords—biometric verification, likelihood ratio, smart gun, grip-pattern recognition I.
Information Leakage of Continuous-Source Zero Secrecy Leakage Helper Data Schemes
"... A Helper Data Scheme is a cryptographic primitive that extracts a high-entropy noise-free string from noisy data. Helper Data Schemes are used for privacy-preserving databases and for Physical Unclonable Functions. We refine the theory of Helper Data schemes with Zero Secrecy Leakage (ZSL), i.e. the ..."
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A Helper Data Scheme is a cryptographic primitive that extracts a high-entropy noise-free string from noisy data. Helper Data Schemes are used for privacy-preserving databases and for Physical Unclonable Functions. We refine the theory of Helper Data schemes with Zero Secrecy Leakage (ZSL), i.e. the mutual information between the helper data and the extracted secret is zero. We prove that ZSL necessitates particular properties of the helper data generating function, which also allows us to show the existence of ‘Sibling Points’. In the special case that our generated secret is uniformly distributed (Fuzzy Extractors) our results coincide with the continuum limit of a recent construction by Verbiskiy et al. Yet our results cover secure sketches as well. Moreover we present an optimal reconstruction algorithm for this scheme, that not only provides the lowest possible reconstruction error rate but also yields an attractive, simple implementation of the verification. Further, we introduce Diagnostic Category Leakage (DCL), which quantifies what an attacker can infer from helper data about a particular medical indication of the enrolled user, or reversely what probabilistic knowledge of a diagnose can leak about the secret. If the attacker has a priori knowledge about the enrolled user (medical indications, race, gender), then the ZSL property does not guarantee that there is no secrecy leakage from the helper data. However, this effect is typically very small.
Grip-Pattern Verification for Smart Gun Based on Maximum-Pairwise Comparison and Mean-Template Comparison,” Paper presented at the
- IEEE Second International Conference on Biometrics: Theory, Applications, and Systems
, 2008
"... Abstract- In our biometric verification system of a smart gun, the rightful user of a gun is authenticated by grip-pattern recognition. In this work verification will be done using two types of comparison methods, respectively. One is mean-template comparison, where the matching score between a test ..."
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Abstract- In our biometric verification system of a smart gun, the rightful user of a gun is authenticated by grip-pattern recognition. In this work verification will be done using two types of comparison methods, respectively. One is mean-template comparison, where the matching score between a test image and a subject is computed, by comparing the test image to the mean value of training samples of this subject. The other one is maximum-pairwise comparison, where the matching score between a test image and a subject is selected as the maximum, among all the similarity scores resulting from comparison between the test image and each training sample of this subject. Experimental results show that a much lower false-acceptance rate can be obtained at the required false-rejection rate of our system us-ing maximum-pairwise comparison, than mean-template comparison. I.
Better than best: matching score based face registration
"... For most face recognition systems, proper registration of the faces to a common coordinate system is of great importance to obtain acceptable performance. Gen-erally, for this purpose, landmarks are selected that can be located reliably, like the centres of the eyes, the tip of the nose and the cent ..."
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For most face recognition systems, proper registration of the faces to a common coordinate system is of great importance to obtain acceptable performance. Gen-erally, for this purpose, landmarks are selected that can be located reliably, like the centres of the eyes, the tip of the nose and the centre or corners of the mouth. Many of the published results of face recognition methods are based on registra-tion using manually selected landmarks, which is often regarded as the ”gold standard”. In this paper we show that using a matching score based registration approach, we can significantly improve upon face identification and verification results obtained using registration with manual landmarks. For recognition using PCA/Mahanalobis Cosine, we obtained up to 9 % improvement in identification rate on the FERET data base. 1
Fast and Accurate 3D Face Recognition Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region Classifiers
, 2010
"... this paper we present a new robust approach for 3D face registration to an intrinsic coordinate system of the face. The intrinsic coordinate system is defined by the vertical symmetry plane through the nose, the tip of the nose and the slope of the bridge of the nose. In addition, we propose a 3D fa ..."
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this paper we present a new robust approach for 3D face registration to an intrinsic coordinate system of the face. The intrinsic coordinate system is defined by the vertical symmetry plane through the nose, the tip of the nose and the slope of the bridge of the nose. In addition, we propose a 3D face classifier based on the fusion of many dependent region classifiers for overlapping face regions. The region classifiers use PCA-LDA for feature extraction and the likelihood ratio as a matching score. Fusion is re-alised using straightforward majority voting for the identifi-cation scenario. For verification, a voting approach is used as well and the decision is defined by comparing the num-ber of votes to a threshold. Using the proposed registration method combined with a classifier consisting of 60 fused re-gion classifiers we obtain a 99.0 % identification rate on the all vs first identification test of the FRGC v2 data. A verifi-cation rate of 94.6 % at FAR = 0.1 % was obtained for the all vs all verification test on the FRGC v2 data using fusion of 120 region classifiers. The first is the highest reported per-formance and the second is in the top-5 of best performing systems on these tests. In addition, our approach is much faster than other methods, taking only 2.5 seconds per im-age for registration and less than 0.1 ms per comparison. Because we apply feature extraction using PCA and LDA, the resulting template size is also very small: 6 kB for 60 region classifiers.
JOURNAL IEEE TRANSACTION ON SYSTEMS, MAN AND CYBERNETICS- PART A 1 Binary Biometrics: An Analytic Framework to Estimate the Performance Curves under Gaussian Assumption.
"... Abstract—In recent years the protection of biometric data has gained increased interest from the scientific community. Methods such as the fuzzy commitment scheme, helper data system, fuzzy extractors, fuzzy vault and cancellable biometrics have been proposed for protecting biometric data. Most of t ..."
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Abstract—In recent years the protection of biometric data has gained increased interest from the scientific community. Methods such as the fuzzy commitment scheme, helper data system, fuzzy extractors, fuzzy vault and cancellable biometrics have been proposed for protecting biometric data. Most of these methods use cryptographic primitives or error-correcting codes (ECC) and use a binary representation of the real-valued biometric data. Hence, the difference between two biometric samples is given by the Hamming distance or bit errors between the binary vectors obtained from the enrollment and verification phases respectively. If the Hamming distance is smaller (larger) than the decision threshold, then the subject is accepted (rejected) as genuine. Because of the use of ECCs, this decision threshold is limited to the maximum error-correcting capacity of the code, consequently limiting the false rejection rate (FRR) and false acceptance rate (FAR) trade-off. A method to improve the FRR consists in using multiple biometric samples in either the enrollment or verification phase. The noise is suppressed, hence reducing the number of bit errors and decreasing the Hamming distance. In practice, the number of samples is empirically chosen without fully considering its fundamental impact. In this work, we present a Gaussian analytical framework for estimating the performance of a binary biometric system given the number of samples being used in the enrollment and the verification phase. The error detection trade-off (DET) curve that combines the false acceptance and false rejection rates is estimated to assess the system performance. The analytic expressions are validated using the FRGC v2 and FVC2000 biometric databases. Index Terms—Binary biometrics, Binary template matching, Performance estimation, Template protection.
