RSA Cryptography based Multi-Modal Biometric Identification System for High-Security Application

Touch gesture biometrics authentication system is the study of user's touching behavior on his touch device to identify him. The features traditionally used in touch gesture authentication systems are extracted using hand-crafted feature extraction approach. In this work, we investigate the ability of Deep Learning (DL) to automatically discover useful features of touch gesture and use them to authenticate the user. Four different models are investigated Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN) combined with LSTM (CNN-LSTM), and CNN combined with GRU (CNN-GRU). In addition, different regularization techniques are investigated such as Activity Regularizer, Batch Normalization (BN), Dropout, and LeakyReLU. These deep networks were trained from scratch and tested using TouchAlytics and BioIdent datasets for dynamic touch authentication. The result reported in terms of authentication accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR). The best result we have been obtained was 96.73%, 96.07% and 96.08% for training, validation and testing accuracy respectively with dynamic touch authentication system on TouchAlytics dataset with CNN-GRU DL model, while the best result of FAR and FRR obtained on TouchAlytics dataset was with CNN-LSTM were FAR was 0.0009 and FRR was 0.0530. For BioIdent dataset the best results have been obtained was 84.87%, 78.28% and 78.35% for Training, validation and testing accuracy respectively with CNN-LSTM model. The use of a learning based approach in touch authentication system has shown good results comparing with other state-of-the-art using TouchAlytics dataset.

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“…-False Rejection Ratio (FRR): It is a part of correct samples that are incorrectly rejected and calculated as Eq. ( 15) [36]:…”

Section: Evaluation Metricmentioning

confidence: 99%

Evaluation of Deep Learning Models for Person Authentication Based on Touch Gesture

Bajaber¹,

Fadel²,

Elrefaei³

2022

Computer Systems Science and Engineering

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“…Others combine two template protection techniques [11]. Additionally, some works use classical encryption methods, such as Rivest-Shamir-Adleman (RSA) [10,25].…”

Section: Related Workmentioning

confidence: 99%

“…Vasavi et al [10] used RSA encryption to protect the face, iris, and palm biometric templates with a public key, which is randomly generated to come up with a cipher text. The authors extract their features using empirical mode decomposition (EMD).…”

Section: Related Workmentioning

confidence: 99%

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Biometric Template Protection for Dynamic Touch Gestures Based on Fuzzy Commitment Scheme and Deep Learning

Bajaber

Elrefaei

2022

Mathematics

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Privacy plays an important role in biometric authentication systems. Touch authentication systems have been widely used since touch devices reached their current level of development. In this work, a fuzzy commitment scheme (FCS) is proposed based on deep learning (DL) to protect the touch-gesture template in a touch authentication system. The binary Bose–Ray-Chaudhuri code (BCH) is used with FCS to deal with touch variations. The BCH code is described by the triplet (n, k, t) where n denotes the code word’s length, k denotes the length of the key and t denotes error-correction capability. In our proposed system, the system performance is investigated using different lengths k. The learning-based approach is applied to extract touch features from raw touch data, as the recurrent neural network (RNN) is used based on a convolutional neural network (CNN). The proposed system has been evaluated on two different touch datasets: the Touchalytics dataset and BioIdent dataset. The best results obtained were with a key length k = 99 and n = 255; the false accept rate (FAR) was 0.00 and false reject rate (FRR) was 0.5854 for the Touchalytics dataset, while the FAR was 0.00 and FRR was 0.5399 with the BioIdent dataset. The FCS shows its effectiveness in dynamic authentication systems, as good results are obtained and compared with other works.

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“…The key for the encryption process is known as the public key and the key used for decryption is the private key. Both are key pairs that will be used in securing messages or information [23], [24]. The steps of the hybrid algorithm are shown in Figure 1, which is described as follows; − The plaintext is original or initial messages like human language in general.…”

Section: Proposed Hybrid Algorithmmentioning

confidence: 99%

Improvement security in e-business systems using hybrid algorithm

Sumaryanti

Kusuma²,

Widijastuti

et al. 2021

TELKOMNIKA

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E-business security becomes an important issue in the development of technology, to ensure the safety and comfort of transactions in the exchange of information is privacy. This study aims to improve security in e-business systems using a hybrid algorithm that combines two types of keys, namely symmetric and asymmetric keys. Encryption and decryption of messages or information carried by a symmetric key using the simple symmetric key algorithm and asymmetric keys using the Rivest Shamir Adleman (RSA) algorithm. The proposed hybrid algorithm requires a high running time in the decryption process compared to the application of a single algorithm. The level of security is stronger because it implements the process of message encryption techniques with two types of keys simultaneously.

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RSA Cryptography based Multi-Modal Biometric Identification System for High-Security Application

Cited by 6 publications

References 13 publications

Evaluation of Deep Learning Models for Person Authentication Based on Touch Gesture

Evaluation of Deep Learning Models for Person Authentication Based on Touch Gesture

Biometric Template Protection for Dynamic Touch Gestures Based on Fuzzy Commitment Scheme and Deep Learning

Improvement security in e-business systems using hybrid algorithm

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