Cancelable face and fingerprint recognition based on the 3D jigsaw transform and optical encryption

elazm, Lamiaa A. Abou; Ibrahim, Sameh A.; Egila, Mohamed G.; Shawky, Hamed; Elsaid, Mohamed; El‐Shafai, Walid; El‐Samie, Fathi E. Abd

doi:10.1007/s11042-019-08462-8

Cited by 37 publications

(8 citation statements)

References 46 publications

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“…2 gives the average EER values for the proposed algorithm compared to those of the algorithms given in Refs. [8,10,11,16,19]. It is clear from the table that the proposed algorithm gives low EER values, which means a good performance.…”

Section: Resultsmentioning

confidence: 90%

“…Table 2: Average EER values of the proposed cancelable algorithm and the previous cancelable biometric algorithms in [8,10,11,16,19] Cancelable algorithm EER Proposed 1.9750 × 10 -20 Ref. [8] 0.04 Ref.…”

Section: Resultsmentioning

confidence: 99%

“…The non-invertible transformation key and face feature set are merged together by utilizing the fuzzy commitment. Abouelazm et al [11] presented a multimodal cancelable biometric scheme, which adopts optical encryption and the 3D jigsaw transform. Face and fingerprint are the considered biometrics.…”

Section: Related Workmentioning

confidence: 99%

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Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

El‐Gazar¹,

El‐Shafai²,

El‐Banby³

et al. 2022

Computer Systems Science and Engineering

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Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last decades. It achieves safe access to systems using biometrics instead of the traditional passwords. The utilization of a biometric in its original format makes it usable only once. Therefore, a cancelable biometric template should be used, so that it can be replaced when it is attacked. Cancelable biometrics aims to enhance the security and privacy of biometric authentication. Digital encryption is an efficient technique to be used in order to generate cancelable biometric templates. In this paper, a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification systems. The considered biometric in this paper is the speech signal. The speech signal is transformed into its spectrogram. Then, the spectrogram is encrypted using two cascaded optical encryption algorithms. The first algorithm is the Optical Scanning Holography (OSH) for its efficiency as an encryption tool. The OSH encrypted spectrogram is encrypted using Double Random Phase Encoding (DRPE) by implementing two Random Phase Masks (RPMs). After the two cascaded optical encryption algorithms, the cancelable template is obtained. The verification is implemented through correlation estimation between enrolled and test templates in their encrypted format. If the correlation value is larger than a threshold value, the user is authorized. The threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two curves. The implementation of optical encryption is adopted using its software rather than the optical setup. The efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation results. It can improve the biometric data security without deteriorating the recognition accuracy. Simulation results give close-to-zero

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Section: Resultsmentioning

confidence: 90%

Section: Resultsmentioning

confidence: 99%

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Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

El‐Gazar¹,

El‐Shafai²,

El‐Banby³

et al. 2022

Computer Systems Science and Engineering

Self Cite

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“…On the other hand, strong encryption algorithms can be used to generate encrypted biometric templates to be stored in the databases for the recognition task. A good property that should be achieved in cancellable biometric systems is the pattern diversity with unlinkability between patterns to use different patterns for different applications [3]. These properties can be guaranteed through changing the initial parameters or keys of the used transforms or encryption algorithms [4].…”

Section: Introductionmentioning

confidence: 99%

Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

elazm¹,

El‐Shafai²,

Ibrahim³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Biometric security is a growing trend, as it supports the authentication of persons using confidential biometric data. Most of the transmitted data in multimedia systems are susceptible to attacks, which affect the security of these systems. Biometric systems provide sufficient protection and privacy for users. The recently-introduced cancellable biometric recognition systems have not been investigated in the presence of different types of attacks. In addition, they have not been studied on different and large biometric datasets. Another point that deserves consideration is the hardware implementation of cancellable biometric recognition systems. This paper presents a suggested hybrid cancellable biometric recognition system based on a 3D chaotic cryptosystem. The rationale behind the utilization of the 3D chaotic cryptosystem is to guarantee strong encryption of biometric templates, and hence enhance the security and privacy of users. The suggested cryptosystem adds significant permutation and diffusion to the encrypted biometric templates. We introduce some sort of attack analysis in this paper to prove the robustness of the proposed cryptosystem against attacks. In addition, a Field Programmable Gate Array (FPGA) implementation of the proposed system is introduced. The obtained results with the proposed cryptosystem are compared with those of the traditional encryption schemes, such as Double Random Phase Encoding (DRPE) to reveal superiority, and hence high recognition performance of the proposed cancellable biometric recognition system. The obtained results prove that the proposed cryptosystem enhances the security and leads to better efficiency of the cancellable biometric recognition system in the presence of different types of attacks.

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“…fingerprint, iris, face, hand-geometry, etc. ), [4][5][6][7][8] signalbased (e.g. voice, electrocardiography [ECG], electroencephalography [EEG], photoplethysmography [PPG], etc.…”

Section: Introductionmentioning

confidence: 99%

Biosignal classification for human identification based on convolutional neural networks

Siam

Sedik

El‐Shafai

et al. 2021

Int J Communication

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Human identification is considered as a serious challenge for several applications such as cybersecurity and access control. Recently, the trend of human identification has been directed to human biometrics, which can be used to recognize persons based on some physiological or behavioral characteristics that they own, such as fingerprint, iris, and biosignals. There are several types of human biosignals including electroencephalography (EEG), electrocardiography (ECG), and photoplethysmography (PPG) signals. This paper presents a human identification system based on PPG signals. The proposed system consists of three main phases: signal acquisition, signal pre‐processing, and feature extraction/classification. The pre‐processing phase involves denoising of the acquired signal, transformation of the 1D signal sequence into a 2D image, and computation of the spectrogram. Feature extraction is carried out on the images obtained from the pre‐processing phase. Features are extracted from the images based on convolutional neural networks (CNNs). The proposed CNN model consists of a sequence of convolutional (CNV) and pooling layers. Finally, the obtained feature maps are fed to the classifier to discriminate human identities. The proposed identification algorithm is applied on signals with and without an additive white Gaussian noise (AWGN). The simulation results reveal that the proposed algorithm achieves an accuracy of 99.5% with the spectrogram representation and 89.8% with the 2D image representation, in the absence of noise. In addition, the paper gives a discussion of the efficiency of denoising techniques such as wavelet denoising, Savitzky–Golay and Kalman filtering, when involved with the proposed algorithm. The simulation results prove that the wavelet dencoising technique has a best performance among the discussed noise reduction techniques.

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Cancelable face and fingerprint recognition based on the 3D jigsaw transform and optical encryption

Cited by 37 publications

References 46 publications

Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

Efficient Hardware Design of a Secure Cancellable Biometric Cryptosystem

Biosignal classification for human identification based on convolutional neural networks

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