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

Punithavathi, P.; Geetha, S.

doi:10.11591/ijeecs.v7.i3.pp877-886

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…These cancelable templates can never be inverted to original biometric features. This can be realized from [13]. Hence the proposed method is robust against template inversion attack.…”

Section: A Non-invertibility Analysismentioning

confidence: 99%

“…The enrolment process involves generation of cancelable fingerprint template from binary fingerprint template using the random projection based cancelable transformation technique [13]. The benefits of using random projection based cancelable transformation are highlighted in [13]. To summarize the process, binary fingerprint features are generated from fingerprint image using the technique in [14].…”

Section: A Enrolling Cancelable Fingerprint Template Into Chip In E-mentioning

confidence: 99%

See 1 more Smart Citation

Cancelable Biometric Transformations for E-Passport Security

Punithavathi¹,

Geetha²

2019

IJEAT

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E-Passport or Electronic Passport is comprised of a microprocessor chip which contains biometric data used to authenticate the identity of passport holder. Facial image, fingerprint image and palm-print image are some of the currently standardized biometric modalities, embedded in the E-Passports. The privacy of the biometric data becomes a problem during storage and transmission. The biometric data cannot be revoked or re-issued unlike tokens or passwords, if compromised. The possibility of producing cancelable templates from the biometric features of user has been studied, thus eliminating threats to privacy and security of the biometric features. Cancelable template is obtained from original biometric image after intentional/repeated distortions (based on user-specific key), and thereby providing security to the templates. The distortions can be implemented either at signal or at feature level. The cancelable templates can be revoked or re-issued whenever required just by changing the user-specific key. The probability of applying cancelable transformations to secure the privacy of the biometric features in E-passport has been explored in the article

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“…These cancelable templates can never be inverted to original biometric features. This can be realized from [13]. Hence the proposed method is robust against template inversion attack.…”

Section: A Non-invertibility Analysismentioning

confidence: 99%

Section: A Enrolling Cancelable Fingerprint Template Into Chip In E-mentioning

confidence: 99%

Cancelable Biometric Transformations for E-Passport Security

Punithavathi¹,

Geetha²

2019

IJEAT

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“…In literature, there are many transformations adopted for generating cancelable biometric templates. In [19] the authors propose an non-invertible template transformation approach using random projection technique and Discrete Fourier transformation to shield the binary biometric pseudorandom representations. A Cancelable ECG Biometric based recognition system using Compressive Sensing-Generalized Likelihood Ratio Test had been proposed by Kim & Chun in [20].…”

Section: Preliminary Analysis a Biometric-attacksmentioning

confidence: 99%

A Novel Cancelable FaceHashing Technique Based on Non-Invertible Transformation With Encryption and Decryption Template

et al. 2020

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A novel cancelable FaceHashing technique based on non-invertible transformation with encryption and decryption template has been proposed in this paper. The proposed system has four components: face preprocessing, feature extraction, cancelable feature extraction followed by the classification, and encryption/decryption of cancelable face feature templates. During face preprocessing, the facial region of interest has been extracted out to speed the process for evaluating discriminant features. In feature extraction, some optimization techniques such as Sparse Representation Coding, Coordinate descent, and Block coordinates descent have been employed on facial descriptors to obtain the best representative of those descriptors. The representative descriptors are further arranged in a spatial pyramid matching structure to extract more discriminant and distinctive feature vectors. To preserve them, the existing BioHashing technique has been modified and extended to some higher levels of security attacks and the modified BioHashing technique computes a cancelable feature vector by the combined effect of the facial feature vector and the assigned token correspond to each user. The elements of computed cancelable feature vector are in a numeric form that has been employed to perform both verifications as well as identification task in online while the original facial feature vectors are kept offline either in hard drive or disc. Then, to enhance more security levels and also to preserve the cancelable face features, an RSA based encryptiondecryption algorithm has been introduced. The proposed system has been tested using four benchmark face databases: CASIA-FACE-v5, IITK, CVL, and FERET, and performance are obtained as correct recognition rate and equal error rate. The performance are compared to the state-of-the-art methods for the superiority of the proposed feature extraction technique and individual performance analysis has been performed at all the security levels of the proposed Cancelable FaceHashing Technique. These comparisons show the superiority of the proposed system.

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“…Block Difference Inverse Probability (BDIP) and basic features are calculated in different multiresolutions. Once the features are extracted, random projection [7] and k nearest neighbor (k NN) [8] with distance weighting are used to classify the suspicious areas into real mass or normal parenchyma. Figure 1 from the radiologists about characteristic of background tissue, type of abnormality present, severity of abnormality, the coordinates of center and approximate radius (in pixels) of a circle enclosing the abnormality.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Random Projection, Multi-Resolution Features and Distance Weighted K Nearest Neighbor for Masses Detection in Mammographic Images

Le¹

2018

IJEECS

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<p>Breast cancer is the top cancer in women both in the developed and the developing world. For early detection of the disease, mammography is still the most effective method beside ultrasound and magnetic resonance imaging. Computer Aided Detection systems have been developed to aid radiologists in diagnosing breast cancer. Different methods were proposed to overcome the main drawback of producing large number of False Positives. In this paper, we presented a novel method for masses detection in mammograms. To describe masses, multi-resolution features were utilized. In feature extraction step, we calculated multi-resolution Block Difference Inverse Probability features and multi-resolution statistical features. Once the descriptors were extracted, we deployed random projection and distance weighted K Nearest Neighbor to classify the detected masses. The result is quite sanguine with sensitivity, false positive reduction and time for carrying out the algorithm</p>

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

Cited by 5 publications

References 15 publications

Cancelable Biometric Transformations for E-Passport Security

Cancelable Biometric Transformations for E-Passport Security

A Novel Cancelable FaceHashing Technique Based on Non-Invertible Transformation With Encryption and Decryption Template

Fusion of Random Projection, Multi-Resolution Features and Distance Weighted K Nearest Neighbor for Masses Detection in Mammographic Images

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