Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits

Alay, Nada; Al-Baity, Heyam H.

doi:10.3390/s20195523

Cited by 137 publications

(62 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This mechanism can be used to calculate the average of the two classes of left and right thresholds, which is shown in Equation (10). Using the weights of the two classes and the calculated average, such as Equation (11), it is possible to define an appropriate objective function whose goal is to maximize the optimal threshold [56]: Here, the goal is to find the optimal threshold ** 12 ( , ,..., )…”

Section: Threshold Methodmentioning

confidence: 99%

See 1 more Smart Citation

Human Identity Verification From Biometric Dorsal Hand Vein Images Using the DL-GAN Method

Alashik

Yıldırım

2021

IEEE Access

View full text Add to dashboard Cite

In this research, biometric authentication, which has been widely used for different purposes in the last quarter-century, is studied. Dorsal hand veins are used for biometric authentication. "Deep learning" (DL) and "generative adversarial networks" (GANs) are used together as keys in the study. A DL-GAN is obtained by combining deep learning and GAN. The developed DL-GAN method is tested on two separate databases. The adversarial network (DL-GAN) method is developed to increase the authentication process's proportional value. For identity verification, dorsal hand veins with biometric physical properties are used. A multistep approach is used for selecting hand dorsal features, including preimage processing and effectively identifying individuals. The deep learning productive antinetwork method is used to effectively identify individuals based on the information obtained from the dorsal hand vein images. For the test in the study, two open access databases are used. These databases are the Jilin University -dorsal hand vein database and the 11K hands database. The results of the experiments performed on the dataset related to the dorsal hand vessels show that the DL-GAN method reaches an identity accuracy level of 98.36% and has an error rate of 2.47% and a standard accuracy of 0.19%. The accuracy of the experimental results in the second dataset is 96.43%, the equal error rate is 3.55% and the standard accuracy is 0.21%. The improved DL-GAN method obtains better results than physical biometric methods such as LBP, LPQ, GABOR, FGM, BGM and SIFT.

show abstract

Section: Threshold Methodmentioning

confidence: 99%

“…The iris authentication method has high accuracy, but unfortunately, it faces challenges. Iris biometric detection requires specific cameras, hardware, and substrates necessary for this method and has made its use costly [11]. Palm line information is one of the most widely used methods of identifying individuals.…”

Section: Introductionmentioning

confidence: 99%

Human Identity Verification From Biometric Dorsal Hand Vein Images Using the DL-GAN Method

Alashik

Yıldırım

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Generally speaking, biometrics aims to identify or verify an individual’s identity according to some physical or behavioral characteristics [ 1 ]. Biometric practices replace conventional knowledge-based solutions, such as passwords or PINs, and possession-based strategies, such as ID cards or badges [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition

Adjabi

Ouahabi

Benzaoui

et al. 2021

Sensors

View full text Add to dashboard Cite

Single-Sample Face Recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, mainly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper discusses the relevance of an original method for SSFR, called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF), which exploits several kinds of features, namely, local, regional, global, and textured-color characteristics. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the K-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex and Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior and competitive results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. The average classification accuracies are 96.17% and 99% for the AR database with two specific protocols (i.e., Protocols I and II, respectively), and 38.01% for the challenging LFW database. These performances are clearly superior to those obtained by state-of-the-art methods. Furthermore, the proposed method uses algorithms based only on simple and elementary image processing operations that do not imply higher computational costs as in holistic, sparse or deep learning methods, making it ideal for real-time identification.

show abstract

“…Generally speaking, biometrics aims to identify or verify an individual's identity according to some physical or behavioral characteristics [1]. Biometric practices are replacing conventional knowledge-based solutions, such as passwords or PINs, and possession-based strategies, such as ID cards or badges [2].…”

Section: Introductionmentioning

confidence: 99%

“…end for 11. Concatenate the computed MB-C-BSIF features: 3 = 2 (1) + 2 (2) + 2 (3) 12. Apply K-NN associated with a metric distance Output: Identification decision…”

mentioning

confidence: 99%

Multi-Block Color-Binarized Statistical Images for Single Sample Face Recognition

Adjabi¹,

Benzaoui²,

Ouahabi³

et al. 2020

Preprint

View full text Add to dashboard Cite

Single sample face recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, particularly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper suggests a different method based on a variant of the Binarized Statistical Image Features (BSIF) descriptor called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF) to resolve the SSFR Problem. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the k-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex &amp; Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. Furthermore, the suggested method employs algorithms with lower computational cost, making it ideal for real-time applications.

show abstract

Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits

Cited by 137 publications

References 35 publications

Human Identity Verification From Biometric Dorsal Hand Vein Images Using the DL-GAN Method

Human Identity Verification From Biometric Dorsal Hand Vein Images Using the DL-GAN Method

Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition

Multi-Block Color-Binarized Statistical Images for Single Sample Face Recognition

Contact Info

Product

Resources

About