Cancelable Template Generation Research Articles

Cancelable template generation based on quantization concepts

Cancelable template generation based on quantization concepts

Efficient Cancelable Template Generation Based on Signcryption and Bio Hash Function

Cancelable biometrics is a demanding area of research in which a cancelable template conforming to a biometric is produced without degrading the efficiency. There are numerous approaches described in the literature that can be used to generate these cancelable templates. These approaches do not, however, perform well in either the qualitative or quantitative perspective. To address this challenge, a unique cancelable template generation mechanism based on signcryption and bio hash function is proposed in this paper. Signcryption is a lightweight cryptographic approach that uses hyper elliptic curve cryptography for encryption and a bio hash function for generating signatures in this proposed method. The cancelable templates are generated from iris biometrics. The hybrid grey level distancing method is used for perfect iris feature extraction for the CASIA and IITD datasets. The proposed approach is compared against the existing state-of-the-art cancelable techniques. The resulting analysis reveals that the proposed method is efficient in terms of accuracy of 98.86%, with lower EER of 0.1%. The average minimum TPR and TNR of the proposed method is about 99.81%.

Cancellable template generation for speaker recognition based on spectrogram patch selection and deep convolutional neural networks

Cancellable template generation for speaker recognition based on spectrogram patch selection and deep convolutional neural networks

A Tokenless Cancellable Scheme for Multimodal Biometric Systems

Biometric template protection (BTP) is an open problem for biometric identity management systems. Cancellable biometrics is commonly designed to protect biometric templates with two input factors i.e., biometrics and a token used in template replacement. However, the token is often required to be kept secretly; otherwise, the protected template could be vulnerable to several security attacks and breaches of privacy. In this paper, we propose a tokenless cancellable biometrics scheme called Multimodal Extended Feature Vector (M∙EFV) Hashing that employs an improved XOR encryption/decryption notion to operate on the transformation key. We stress on multimodal biometrics where the real-valued face and fingerprint vectors are fused and embedded into a binarized cancellable template. Specifically, M∙EFV hashing consists of three stages of transformation: 1) normalization and biometric fusion; 2) randomization and binarization; and 3) cancellable template generation. To evaluate the proposed scheme, several benchmarking datasets, i.e., FVC2002, FVC2004 for fingerprint and LFW for face are used in experiments. The verification performance is validated by employing the FVC matching protocol. Various attacks are simulated and analysed in the worst-case scenario. Lastly, unlinkability and revocability properties are examined experimentally.

Efficient Cancelable Iris Template Generation for Wearable Sensors

When biometric authentication is performed on On-Body Wearable Wireless Networks, a cancelable template is useful to protect biometric information. A cancelable template generation method converts the original biometric information into irreversibly transformed information to protect the original biometric information. If a cancelable template is damaged or leaked, it can be replaced with another cancelable template. In 2017, Dwivedi et al. proposed a novel cancelable iris template generation scheme based on the randomized look-up table mapping. So far their scheme is the most accurate scheme with respect to EER compared to the previous cancelable iris template generation schemes. However, their scheme is not alignment-free and so is not efficient enough for wearable sensors. In the paper, we first suggest how to improve the accuracy of the Dwivedi et al.’s scheme using the partial sort technique. Our experiment result shows that our suggested scheme is more accurate than the Dwivedi et al.’s scheme under almost all parameter settings. More concretely, our scheme achieves EER 0.09%, whereas the Dwivedi et al.’s scheme achieves EER 0.43% in the best parameter settings for the CASIA-V3-Interval iris database. We also suggest how to improve the efficiency of the Dwivedi et al.’s scheme. Our second scheme is alignment-free by processing IrisCode column-wise, whereas the Dwivedi et al.’s scheme handles IrisCode row-wise. Our experiment shows that our second scheme is 15 times faster than the Dwivedi et al.’s scheme, so our scheme is efficient enough for wearable sensors. Though our second scheme has very high EER under some parameter settings, our second scheme achieves EER 0.53% in the best parameter settings for the CASIA-V3-Interval iris database.

Random Projection-based Cancelable Template Generation for Sparsely Distributed Biometric Patterns

Cancelable biometrics, a template transformation approach, attempts to provide robustness for authentication services based on biometrics. Several biometric template protection techniques represent the biometric information in binary form as it provides benefits in matching and storage. In this context, it becomes clear that often such transformed binary representations can be easily compromised and breached. In this paper, we propose an efficient non-invertible template transformation approach using random projection technique and Discrete Fourier transformation to shield the binary biometric representations. The cancelable fingerprint templates designed by the proposed technique meets the requirements of revocability, diversity, non-invertibility and performance. The matching performance of the cancelable fingerprint templates generated using proposed technique, have improved when compared with the state-of-art methods.

Multi-algorithmic fingerprint model for cancellable template generation

In this paper we present a multi-algorithmic biometric system comprising the fingerprint biometrics to tackle highly uncertain databases. Two methods were proposed namely, spiral-based method and block-based method to generate cancellable fingerprint templates. Spiral-based method can be used to exploit elements in each contiguous right angled triangle, that are drawn using the invariant distances between reference minutia to every other minutia. In the block-based method, fingerprint image can be tessellated into blocks to get a set of features by considering Euclidean distance from each minutia in reference block to other minutia in blocks which are horizontal, vertical, and diagonal in direction to reference block, and orientation of minutiae. Two biometric methods are fused at score level based on Yager's triangular norm. The experimental evaluations and analysis are performed using the FVC2002 databases. The results show better performance and separation between genuine and imposter distributions.

Multi-algorithmic fingerprint model for cancellable template generation

Multi-algorithmic fingerprint model for cancellable template generation

Fingerprint template protection using multiline neighboring relation

Security and privacy concern of biometric information leads to the attention for generating cancellable template from the original biometric template. We proposed an alignment free cancellable template generation by construction of M rectangles with different orientations around every reference minutiae followed by the calculation of rotation invariant and translation invariant neighboring relation, plane based quantization for bit string generation and matching. The proposed method fulfills non-invertibility, accuracy, diversity and revocability properties which are the primary requirements of cancellable template design. The EER obtained for FVC 2002 DB1, DB2, and DB3 is 0.62%, 1.33%, and 2.64% for same key scenario. The proposed method shows the better performance for FVC 2002 DB1, DB2, and DB3 compared to the existing methods (Wang & Hu, 2012, 2014; Ahmad et al., 2011).