Gaussian Random Projection Based Non-invertible Cancelable Biometric Templates

Kaur, Harkeerat; Khanna, Pritee

doi:10.1016/j.procs.2015.06.077

Cited by 31 publications

(19 citation statements)

References 19 publications

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“…The RP is adopted in this study as a tool to encrypt the extracted patterns from the original biometrics either with fuzzy processing or homomorphic transformation. In recent implementations of CBS, the RP has been applied on the original biometric templates directly [9,18,[20][21][22]. Both Homomorphic and fuzzy transforms share the common feature of non-invertibility.…”

Section: Gaussian Rpmentioning

confidence: 99%

“…Cancellable biometric frameworks provide templates to safeguard the integrity of biometric data for different applications [3][4][5][6][7][8][9]. In [3], Soliman et al presented a CBS based on Double Random Phase Encoding (DRPE) for applications in both face and iris recognition.…”

Section: Introductionmentioning

confidence: 99%

“…In this system, an extracted low-dimension feature matrix is re-projected randomly to generate a binary bit string. In another interesting contribution [9], Kaur and Khanna reported a CBS using Gaussian random vectors and one-way modulus hashing. This method was utilised in face and palm print identification.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Implementation of Homomorphic and Fuzzy Transforms in Random-Projection Encryption Frameworks for Cancellable Face Recognition

et al. 2020

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To circumvent problems associated with dependence on traditional security systems on passwords, Personal Identification Numbers (PINs) and tokens, modern security systems adopt biometric traits that are inimitable to each individual for identification and verification. This study presents two different frameworks for secure person identification using cancellable face recognition (CFR) schemes. Exploiting its ability to guarantee irrevocability and rich diversity, both frameworks utilise Random Projection (RP) to encrypt the biometric traits. In the first framework, a hybrid structure combining Intuitionistic Fuzzy Logic (IFL) with RP is used to accomplish full distortion and encryption of the original biometric traits to be saved in the database, which helps to prevent unauthorised access of the biometric data. The framework involves transformation of spatial-domain greyscale pixel information to a fuzzy domain where the original biometric images are disfigured and further distorted via random projections that generate the final cancellable traits. In the second framework, cancellable biometric traits are similarly generated via homomorphic transforms that use random projections to encrypt the reflectance components of the biometric traits. Here, the use of reflectance properties is motivated by its ability to retain most image details, while the guarantee of the non-invertibility of the cancellable biometric traits supports the rationale behind our utilisation of another RP stage in both frameworks, since independent outcomes of both the IFL stage and the reflectance component of the homomorphic transform are not enough to recover the original biometric trait. Our CFR schemes are validated on different datasets that exhibit properties expected in actual application settings such as varying backgrounds, lightings, and motion. Outcomes in terms standard metrics, including structural similarity index metric (SSIM) and area under the receiver operating characteristic curve (AROC), suggest the efficacy of our proposed schemes across many applications that require person identification and verification.

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Section: Gaussian Rpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Implementation of Homomorphic and Fuzzy Transforms in Random-Projection Encryption Frameworks for Cancellable Face Recognition

et al. 2020

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“…Teoh and Yaung [11] proposed multispace random projection in which a fixed-length vector acquired from raw biometric features, are projected on a sequence of random sub-spaces derived from userspecific pseudorandom number. Kaur and Khanna [12] proposed random projection for generating cancelable templates of palmprint and face.…”

Section: Literature Surveymentioning

confidence: 99%

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

Punithavathi¹,

Geetha²

2017

IJEECS

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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.

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“…In [10], it proposed a new method for the design of alignment free cancelable fingerprint templates using local minutia structures formed by zoned minutia pairs. A novel cancelable biometric template generation algorithm using Gaussian random vectors and one way modulus hashing was proposed in [11]. In BCs, a key is either bound (key binding schemes) or extracted (key generation schemes) from biometric data [8].…”

Section: Introductionmentioning

confidence: 99%

Research on Face Recognition Method by Autoassociative Memory Based on RNNs

Han

Deng

et al. 2018

Complexity

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In order to avoid the risk of the biological database being attacked and tampered by hackers, an Autoassociative Memory (AAM) model is proposed in this paper. The model is based on the recurrent neural networks (RNNs) for face recognition, under the condition that the face database is replaced by its model parameters. The stability of the model is proved and analyzed to slack the constraints of AAM model parameters. Besides, a design procedure about solving AAM model parameters is given, and the face recognition method by AAM model is established, which includes image preprocessing, AAM model training, and image recognition. Finally, simulation results on two experiments show the feasibility and performance of the proposed face recognition method.

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Gaussian Random Projection Based Non-invertible Cancelable Biometric Templates

Cited by 31 publications

References 19 publications

Efficient Implementation of Homomorphic and Fuzzy Transforms in Random-Projection Encryption Frameworks for Cancellable Face Recognition

Efficient Implementation of Homomorphic and Fuzzy Transforms in Random-Projection Encryption Frameworks for Cancellable Face Recognition

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

Research on Face Recognition Method by Autoassociative Memory Based on RNNs

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