A discriminant binarization transform using genetic algorithm and error-correcting output code for face template protection

Nazari, Sara; Moin, Mohammad Shahram; Kanan, Hamidreza Rashidy

doi:10.1007/s13042-017-0723-3

Cited by 10 publications

(6 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tables 2 and 3 show the comparison results on the Extended YaleB and CMU-PIE datasets, respectively. On the one hand, our method can earn a GAR of 99.40% for 128 bits binary code, which outperforms the Genetic-ECOC method [64] at GAR at a fixed 1% FAR on the Extended YaleB dataset. On the other hand, references [64,66] may perform extremely well; they are close to our performance.…”

Section: Accuracy Performancementioning

confidence: 91%

“…On the one hand, our method can earn a GAR of 99.40% for 128 bits binary code, which outperforms the Genetic-ECOC method [64] at GAR at a fixed 1% FAR on the Extended YaleB dataset. On the other hand, references [64,66] may perform extremely well; they are close to our performance. However, their lengths of the codeword are both less than 90, and as such they offer lower security to brute force attacks.…”

Section: Accuracy Performancementioning

confidence: 91%

“…In general, accuracy performance can be well preserved under the different key lengths and the intra-user variations. To further illustrate the accuracy performance of our methods, we compare our approach with other state-of-art methods [15,[64][65][66] on Extended YaleB and CMU-PIE dataset. Tables 2 and 3 show the comparison results on the Extended YaleB and CMU-PIE datasets, respectively.…”

Section: Accuracy Performancementioning

confidence: 99%

See 2 more Smart Citations

A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application

Wang

Zhang

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

Biometric keys are widely used in the digital identity system due to the inherent uniqueness of biometrics. However, existing biometric key generation methods may expose biometric data, which will cause users’ biometric traits to be permanently unavailable in the secure authentication system. To enhance its security and privacy, we propose a secure biometric key generation method based on deep learning in this paper. Firstly, to prevent the information leakage of biometric data, we utilize random binary codes to represent biometric data and adopt a deep learning model to establish the relationship between biometric data and random binary code for each user. Secondly, to protect the privacy and guarantee the revocability of the biometric key, we add a random permutation operation to shuffle the elements of binary code and update a new biometric key. Thirdly, to further enhance the reliability and security of the biometric key, we construct a fuzzy commitment module to generate the helper data without revealing any biometric information during enrollment. Three benchmark datasets including ORL, Extended YaleB, and CMU-PIE are used for evaluation. The experiment results show our scheme achieves a genuine accept rate (GAR) higher than the state-of-the-art methods at a 1% false accept rate (FAR), and meanwhile satisfies the properties of revocability and randomness of biometric keys. The security analyses show that our model can effectively resist information leakage, cross-matching, and other attacks. Moreover, the proposed model is applied to a data encryption scenario in our local computer, which takes less than 0.5 s to complete the whole encryption and decryption at different key lengths.

show abstract

Section: Accuracy Performancementioning

confidence: 91%

Section: Accuracy Performancementioning

confidence: 91%

Section: Accuracy Performancementioning

confidence: 99%

See 1 more Smart Citation

A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application

Wang

Zhang

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…For the secure sketch, the approaches-based fuzzy commitment [31,32] and fuzzy vault [33][34][35] bind the biometric template with an error correction codeword. Sutcu et al [36] used error correcting codes or signal embeddings to provide informationtheoretic guarantees for security and privacy.…”

Section: Related Workmentioning

confidence: 99%

“…For the secure sketch, the approaches‐based fuzzy commitment [31, 32] and fuzzy vault [33–35] bind the biometric template with an error correction codeword. Sutcu et al .…”

Section: Related Workmentioning

confidence: 99%

Face template protection using deep LDPC codes learning

et al. 2019

View full text Add to dashboard Cite

There is a noticeable tendency to apply deep convolutional neural network (CNN) in facial identification, since it is able to boost performance in face recognition and verification. However, due to the users have unique facial, exposure of face template to adversaries can severely compromise system security and users' privacy. Here, the authors propose a face template protection technique by using multi-label learning, which maps the facials into low-density parity-check (LDPC) codes. Firstly, a random binary sequence is generated to represent a user and further hashed to produce the protected template. During the training, the random binary sequences are encoded by an LDPC encoder to produce diverse binary codes. Based on carefully designed deep multi-label learning, the facial features of each user are mapped to a diverse binary code. In the process of recognition and verification, the deep CNN mapping architecture is modelled as a Gaussian channel, while the noise brought by intra-variations in the outputs of CNN can be removed by the LDPC decoder. Thus, a robust face template protection scheme is achieved. The simulation results on PIE and extended Yale B indicate that the proposed scheme achieves high genuine accept rate at 1% false accept rate.

show abstract

Multiclass classification by Min–Max ECOC with Hamming distance optimization

Szücs

2022

Vis Comput

View full text Add to dashboard Cite

Two questions often arise in the field of the ensemble in multiclass classification problems, (i) how to combine base classifiers and (ii) how to design possible binary classifiers. Error-correcting output codes (ECOC) methods answer these questions, but they focused on only the general goodness of the classifier. The main purpose of our research was to strengthen the bottleneck of the ensemble method, i.e., to minimize the largest values of two types of error ratios in the deep neural network-based classifier. The research was theoretical and experimental, the proposed Min–Max ECOC method suggests a theoretically proven optimal solution, which was verified by experiments on image datasets. The optimal solution was based on the maximization of the lowest value in the Hamming matrix coming from the ECOC matrix. The largest ECOC matrix, the so-called full matrix is always a Min–Max ECOC matrix, but smaller matrices generally do not reach the optimal Hamming distance value, and a recursive construction algorithm was proposed to get closer to it. It is not easy to calculate optimal values for large ECOC matrices, but an interval with upper and lower limits was constructed by two theorems, and they were proved. Convolutional Neural Networks with Min–Max ECOC matrix were tested on four real datasets and compared with OVA (one versus all) and variants of ECOC methods in terms of known and two new indicators. The experimental results show that the suggested method surpasses the others, thus our method is promising in the ensemble learning literature.

show abstract

A discriminant binarization transform using genetic algorithm and error-correcting output code for face template protection

Cited by 10 publications

References 38 publications

A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application

A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application

Face template protection using deep LDPC codes learning

Multiclass classification by Min–Max ECOC with Hamming distance optimization

Contact Info

Product

Resources

About