Fusion of deep-learned and hand-crafted features for cancelable recognition systems

Abdellatef, Essam; Omran, Eman M.; Soliman, Randa F.; Ismail, Nabil A.; Elrahman, Salah Eldin S. E. Abd; Ismail, Khalid N.; Rihan, Mohamed; El‐Samie, Fathi E. Abd; Eisa, Ayman A.

doi:10.1007/s00500-020-04856-1

Cited by 24 publications

(25 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Techniques such as Convolutional Neural Networks (CNNs) are used for ILD and classification [114] in the consistent models. Many studies from the literature used CNN for ILD [56,63,104,105,111]. CNN takes data as input that has a matrix design such as the images [114].…”

Section: Discussionmentioning

confidence: 99%

“…The Accuracy works correctly when the classes are balanced, which means the number of live samples and fake samples are equal [102]. Many authors [10,11,21,56,98,[103][104][105] used the accuracy as the performance measure for evaluating performances of ILD model.…”

Section: E False Positive Ratementioning

confidence: 99%

“…This means the figure for live samples and fake samples are identical. Many authors [10,21,56,[103][104][105], used the Accuracy for evaluating the performances of Liveness Detection model.…”

Section: Rq4 What Are All the Different Evaluation Measures Used For Iris Liveness Detection?mentioning

confidence: 99%

See 2 more Smart Citations

Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions

et al. 2021

View full text Add to dashboard Cite

Biometrics is progressively becoming vital due to vulnerabilities of traditional security systems leading to frequent security breaches. Biometrics is an automated device that studies human beings’ physiological and behavioral features for their unique classification. Iris-based authentication offers stronger, unique, and contactless identification of the user. Iris liveness detection (ILD) confronts challenges such as spoofing attacks with contact lenses, replayed video, and print attacks, etc. Many researchers focus on ILD to guard the biometric system from attack. Hence, it is vital to study the prevailing research explicitly associated with the ILD to address how developing technologies can offer resolutions to lessen the evolving threats. An exhaustive survey of papers on the biometric ILD was performed by searching the most applicable digital libraries. Papers were filtered based on the predefined inclusion and exclusion criteria. Thematic analysis was performed for scrutinizing the data extracted from the selected papers. The exhaustive review now outlines the different feature extraction techniques, classifiers, datasets and presents their critical evaluation. Importantly, the study also discusses the projects, research works for detecting the iris spoofing attacks. The work then realizes in the discovery of the research gaps and challenges in the field of ILD. Many works were restricted to handcrafted methods of feature extraction, which are confronted with bigger feature sizes. The study discloses that dep learning based automated ILD techniques shows higher potential than machine learning techniques. Acquiring an ILD dataset that addresses all the common Iris spoofing attacks is also a need of the time. The survey, thus, opens practical challenges in the field of ILD from data collection to liveness detection and encourage future research.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: E False Positive Ratementioning

confidence: 99%

See 1 more Smart Citation

Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions

et al. 2021

View full text Add to dashboard Cite

show abstract

“…It achieved a high-performance detection rate, with more than 99% accuracy. A study by [95] proposed a fusion of network of deep features extracted using deep CNN and handcrafted features for concealable biometrics. An integrated biometric recognition was used including face, iris, palm print, fingerprint, and ear biometrics.…”

Section: Application Of Convolutional Neural Network In Fingerprint Image Analysismentioning

confidence: 99%

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Chiroma¹

2021

AIS

View full text Add to dashboard Cite

Deep Learning algorithms (DL) have been applied in different domains such as computer vision, image detection, robotics and speech processing, in most cases, DL demonstrated better performance than the conventional machine learning algorithms (shallow algorithms). The artificial intelligence research community has leveraged the robustness of the DL because of their ability to process large data size and handle variations in biometric data such as aging or expression problem. Particularly, DL research in automatic fingerprint recognition system (AFRS) is gaining momentum starting from the last decade in the area of fingerprint pre-processing, fingerprints quality enhancement, fingerprint feature extraction, security of fingerprint and performance improvement of AFRS. However, there are limited studies that address the application of DL to model fingerprint biometric for different tasks in the fingerprint recognition process. To bridge this gap, this paper presents a systematic literature review and an insightful meta-data analysis of a decade applications of DL in AFRS. Discussion on proposed model’s tasks, state of the art study, dataset, and training architecture are presented. The Convolutional Neural Networks models were the most saturated models in developing fingerprint biometrics authentication. The study revealed different roles of the DL in training architecture of the models: feature extractor, classifier and end-to-end learning. The review highlights open research challenges and present new perspective for solving the challenges in the future. The author believed that this paper will guide researchers in propose novel fingerprint authentication scheme.

show abstract

“…Unfortunately, it can be hacked if the convolution mask is known and a strong deconvolution algorithm is used. Abdelatif et al developed their approach by incorporating hand-crafted features with their deep features [ 30 ]. They extract hand-crafted features from both face and iris images and apply a dimensionality reduction stage based on PCA to create features suitable in length to the deep features.…”

Section: Introductionmentioning

confidence: 99%

Discrete Transforms and Matrix Rotation Based Cancelable Face and Fingerprint Recognition for Biometric Security Applications

Algarni

Banby

Ismail

et al. 2020

Entropy

View full text Add to dashboard Cite

The security of information is necessary for the success of any system. So, there is a need to have a robust mechanism to ensure the verification of any person before allowing him to access the stored data. So, for purposes of increasing the security level and privacy of users against attacks, cancelable biometrics can be utilized. The principal objective of cancelable biometrics is to generate new distorted biometric templates to be stored in biometric databases instead of the original ones. This paper presents effective methods based on different discrete transforms, such as Discrete Fourier Transform (DFT), Fractional Fourier Transform (FrFT), Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT), in addition to matrix rotation to generate cancelable biometric templates, in order to meet revocability and prevent the restoration of the original templates from the generated cancelable ones. Rotated versions of the images are generated in either spatial or transform domains and added together to eliminate the ability to recover the original biometric templates. The cancelability performance is evaluated and tested through extensive simulation results for all proposed methods on a different face and fingerprint datasets. Low Equal Error Rate (EER) values with high AROC values reflect the efficiency of the proposed methods, especially those dependent on DCT and DFrFT. Moreover, a comparative study is performed to evaluate the proposed method with all transformations to select the best one from the security perspective. Furthermore, a comparative analysis is carried out to test the performance of the proposed schemes with the existing schemes. The obtained outcomes reveal the efficiency of the proposed cancelable biometric schemes by introducing an average AROC of 0.998, EER of 0.0023, FAR of 0.008, and FRR of 0.003.

show abstract

Fusion of deep-learned and hand-crafted features for cancelable recognition systems

Cited by 24 publications

References 47 publications

Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions

Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

Discrete Transforms and Matrix Rotation Based Cancelable Face and Fingerprint Recognition for Biometric Security Applications

Contact Info

Product

Resources

About