Multibiometric secure system based on deep learning

Talreja, Veeru; Valenti, Matthew C.; Nasrabadi, Nasser M.

doi:10.1109/globalsip.2017.8308652

Cited by 73 publications

(35 citation statements)

References 20 publications

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“…Providing auxiliary information to a CNN-based face recognition model can improve its recognition performance; however, in some cases such information is available only during training and may not be available during the testing phase. Despite the potential advantages of using auxiliary data, these problems have diminished the popularity and flexibility of using both soft and hard modalities for biometric applications [30].…”

Section: Introductionmentioning

confidence: 99%

A Deep Face Identification Network Enhanced by Facial Attributes Prediction

Taherkhani

Nasrabadi

Dawson

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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In this paper, we propose a new deep framework which predicts facial attributes and leverage it as a soft modality to improve face identification performance. Our model is an end to end framework which consists of a convolutional neural network (CNN) whose output is fanned out into two separate branches; the first branch predicts facial attributes while the second branch identifies face images. Contrary to the existing multi-task methods which only use a shared CNN feature space to train these two tasks jointly, we fuse the predicted attributes with the features from the face modality in order to improve the face identification performance. Experimental results show that our model brings benefits to both face identification as well as facial attribute prediction performance, especially in the case of identity facial attributes such as gender prediction. We tested our model on two standard datasets annotated by identities and face attributes. Experimental results indicate that the proposed model outperforms most of the current existing face identification and attribute prediction methods.

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Section: Introductionmentioning

confidence: 99%

A Deep Face Identification Network Enhanced by Facial Attributes Prediction

Taherkhani

Nasrabadi

Dawson

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Self Cite

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“…This interaction of client and server within a protected environment helps to preserve their privacy. A secure system for multi-biometric data has been proposed in [8] that uses deep neural networks and error-correction coding. The multi-biometric data is generated by a feature-level fusion framework with the input of multiple biometric data.…”

Section: Related Workmentioning

confidence: 99%

Utilizing Transfer Learning and Homomorphic Encryption in a Privacy Preserving and Secure Biometric Recognition System

2018

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Biometric verification systems have become prevalent in the modern world with the wide usage of smartphones. These systems heavily rely on storing the sensitive biometric data on the cloud. Due to the fact that biometric data like fingerprint and iris cannot be changed, storing them on the cloud creates vulnerability and can potentially have catastrophic consequences if these data are leaked. In the recent years, in order to preserve the privacy of the users, homomorphic encryption has been used to enable computation on the encrypted data and to eliminate the need for decryption. This work presents DeepZeroID: a privacy-preserving cloud-based and multiple-party biometric verification system that uses homomorphic encryption. Via transfer learning, training on sensitive biometric data is eliminated and one pre-trained deep neural network is used as feature extractor. By developing an exhaustive search algorithm, this feature extractor is applied on the tasks of biometric verification and liveness detection. By eliminating the need for training on and decrypting the sensitive biometric data, this system preserves privacy, requires zero knowledge of the sensitive data distribution, and is highly scalable. Our experimental results show that DeepZeroID can deliver 95.47% F1 score in the verification of combined iris and fingerprint feature vectors with zero true positives and with a 100% accuracy in liveness detection.

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“…Sutcu et al [35] evaluated feature string for face and fingerprint biometric and fed it as the input to a fuzzy commitment framework after concatenating both strings. Talreja et al [36] introduced a secure multibiometric framework which evaluates features from face and iris using a Deep neural network, binarize the features and extract reliable bits. Next, error-correction is applied to the combined reliable bits, and the hash of the error-corrected bits is stored as a cancelable template.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, there is no mode present to re-transform it with a new key if multi-biometric template gets compromised. Though the methods proposed in [6,36] shows a significant performance improvement, they are too complex for real-world implications. Besides feature level, there have been various methods [22,15,18,9,25] incorporating score fusion in recent years.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid score level and decision level fusion scheme for cancelable multi-biometric verification

Dwivedi

Dey

2018

Appl Intell

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In spite of the benefits of biometric-based authentication systems, there are few concerns raised because of the sensitivity of biometric data to outliers, low performance caused due to intra-class variations and privacy invasion caused by information leakage. To address these issues, we propose a hybrid fusion framework where only the protected modalities are combined to fulfill the requirement of secrecy and performance improvement. This paper presents a method to integrate cancelable modalities utilizing mean-closure weighting (MCW) score level and Dempster-Shafer (DS) theory based decision level fusion for iris and fingerprint to mitigate the limitations in the individual score or decision fusion mechanisms. The proposed hybrid fusion scheme incorporates the similarity scores from different matchers corresponding to each protected modality. The individual scores obtained from different matchers for each modality are combined using MCW score fusion method. The MCW technique achieves the optimal weight for each matcher involved in the score computation. Further, DS theory is applied to the induced scores to output the final decision. The rigorous experimental evaluations on three virtual databases indicate that the proposed hybrid fusion framework outperforms over the component level or individual fusion methods (score level and decision level fusion). As a result, we achieve (48%,66%), (72%,86%) and (49%,38%) of performance improvement over unimodal cancelable iris and unimodal cancelable fingerprint verification systems for Virtual A, Virtual B and Virtual C databases, respectively. Also, the proposed method is robust enough to the variability of scores and outliers satisfying the requirement of secure authentication.

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Multibiometric secure system based on deep learning

Cited by 73 publications

References 20 publications

A Deep Face Identification Network Enhanced by Facial Attributes Prediction

A Deep Face Identification Network Enhanced by Facial Attributes Prediction

Utilizing Transfer Learning and Homomorphic Encryption in a Privacy Preserving and Secure Biometric Recognition System

A novel hybrid score level and decision level fusion scheme for cancelable multi-biometric verification

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