The relation between the secrecy rate of biometric template protection and biometric recognition performance

The authors explore the possibilities of a dense model-free three-dimensional (3D) face reconstruction method, based on image sequences from a single camera, to improve the current state of forensic face comparison. They propose a new model-free 3D reconstruction method for faces, based on the Lambertian reflectance model to estimate the albedo and to refine the 3D shape of the face. This method avoids any form of bias towards face models and is therefore suitable in a forensic face comparison process. The proposed method can reconstruct frontal albedo images, from multiple nonfrontal images. Also a dense 3D shape model of the face is reconstructed, which can be used to generate faces under pose. In the authors' experiments, the proposed method is able to improve the face recognition scores in more than 90% of the cases. Using the likelihood ratio framework, they show for the same experiment that for data initially unsuitable for forensic use, the reconstructions become meaningful in a forensic context in more than 60% of the cases.

show abstract

“…8, a recreated FRR and FAR graph is shown. The likelihood ratios can be calculated from this graph by applying (7) to each score s , see [21]

LR false(s false) = \frac{1 - false(normal∂ false(FRR false) / normal∂ s false)}{false(normal∂ false(FAR false) / normal∂ s false)}

…”

Section: Methodsmentioning

confidence: 99%

Face reconstruction from image sequences for forensic face comparison

2016

View full text Add to dashboard Cite

show abstract

“…Biometric system performance assessment mainly relies on two key error rates: False Non-Match Rate (FNMR) and False Match Rate (FMR) [14]. FNMR quantifies the system's tendency to incorrectly reject a genuine user's biometric sample, while FMR reflects the incidence of erroneously accepting an imposter's biometric sample as belonging to a legitimate user which is particularly crucial, as highlighted by Veldhuis [44].…”

Section: Metricsmentioning

confidence: 99%

Deep Multi-Biometric Fuzzy Commitment Scheme: Fusion Methods and Performance

Fohr,

Rathgeb

2024

Preprint

View full text Add to dashboard Cite

Biometric cryptosystems offer privacy-preserving authentication using biometric data, such as fingerprints or iris scans. However, single modalities suffer from limited entropy, impacting both recognition performance and security. This work investigates the fusion of multiple biometric characteristics to address these limitations. This study provides a detailed description of the Deep Multi-biometric Fuzzy Commitment Scheme and its components. In the experimental setup, we demonstrate how Convolutional Neural Networks (CNNs) are used to tackle the challenge of non-uniform representation vectors by generating uniform embeddings. This process generates the monomodal databases of iris and fingerprint vectors, as well as the multibiometric database. We then evaluate our three proposed fusion methods: concatenation, interleaving, and random shuffling within the fuzzy commitment scheme using error correction methods such as Hadamard and Reed-Solomon codes. The evaluation of performance and security reveals that random shuffling outperforms other methods like interleaving and concatenation in terms of recognition performance. Concatenation displayed the lowest performance. Finally, the findings are summarized and potential improvements are discussed.

show abstract

“…Obviously, there is a relation between the FMR and the security of a fuzzy vault scheme [36]. The False Accept Security (FAS) 1 provides a good approximation of the security of a fuzzy vault scheme because the false accept attack is typically one of the most efficient attacks.…”

Section: A General Security Measuresmentioning

confidence: 99%

Multi-Biometric Fuzzy Vault based on Face and Fingerprints

Rathgeb¹,

Tams²,

Merkle³

et al. 2023

Preprint

View full text Add to dashboard Cite

The fuzzy vault scheme has been established as cryptographic primitive suitable for privacy-preserving biometric authentication. To improve accuracy and privacy protection, biometric information of multiple characteristics can be fused at feature level prior to locking it in a fuzzy vault. We construct a multi-biometric fuzzy vault based on face and multiple fingerprints. On a multi-biometric database constructed from the FRGCv2 face and the MCYT-100 fingerprint databases, a perfect recognition accuracy is achieved at a false accept security above 30 bits. Further, we provide a formalisation of feature-level fusion in multi-biometric fuzzy vaults, on the basis of which relevant security issues are elaborated. Said security issues, for which we define countermeasures, are commonly ignored and may impair the overall system's security.

show abstract

The relation between the secrecy rate of biometric template protection and biometric recognition performance

Cited by 5 publications

References 31 publications

Face reconstruction from image sequences for forensic face comparison

Face reconstruction from image sequences for forensic face comparison

Deep Multi-Biometric Fuzzy Commitment Scheme: Fusion Methods and Performance

Multi-Biometric Fuzzy Vault based on Face and Fingerprints

Contact Info

Product

Resources

About