Finger vein recognition based on deep learning

Liu, Wenjie; Li, Weijun; Sun, Linjun; Zhang, Liping; Chen, Peng

doi:10.1109/iciea.2017.8282842

Cited by 66 publications

(34 citation statements)

References 17 publications

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“…A ResNet pretrained model was used, and the two traits fused at the score level using several fusion methods, which were weighted sum, weighted product, Bayesian rule, and perceptron rule. Moreover, Liu et al [ 19 ] used the CNN algorithm to build a recognition system that received multiple finger vein images. They developed the CNN model based on the AlexNet pertained model.…”

Section: Related Workmentioning

confidence: 99%

“…However, these deep learning-based approaches, while effective, are very computationally expensive and time consuming [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. In addition, studies deploying deep learning algorithms in multimodal biometric systems [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ] begin the experimentation process by applying region detection methods prior to entering data into deep learning model. The use of region detection methods requires first selecting a suitable technique for a particular trait, also, the process can be time-consuming [ 1 ].…”

Section: Related Workmentioning

confidence: 99%

“…Recently, deep learning has made a considerable impact and produced excellent results in biometrics systems [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. The deep learning algorithms have overcome many of the limitations of other machine learning algorithms, particularly those associated with feature extraction techniques.…”

Section: Introductionmentioning

confidence: 99%

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

Alay¹,

Al-Baity

2020

Sensors

137

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With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Alay¹,

Al-Baity

2020

Sensors

137

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“…Fusion fall under four different levels they are sensor, feature, score, and decision. Recent research is more attractive towards the use of the deep learning method since it can handle limitations that are facing by machine learning techniques [5][6][7]. In the proposed work, a new multimodal biometric recognition modal is explored, which uses multi-level HOG feature fusion of signature and fingerprint with deep learning algorithm for recognition.…”

Section: Introductionmentioning

confidence: 99%

Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learning techniques

Channegowda

Prakash

2021

IJEECS

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Providing security in biometrics is the major challenging task in the current situation. A lot of research work is going on in this area. Security can be more tightened by using complex security systems, like by using more than one biometric trait for recognition. In this paper multimodal biometric models are developed to improve the recognition rate of a person. The combination of physiological and behavioral biometrics characteristics is used in this work. Fingerprint and signature biometrics characteristics are used to develop a multimodal recognition system. Histograms of oriented gradients (HOG) features are extracted from biometric traits and for these feature fusions are applied at two levels. Features of fingerprint and signatures are fused using concatenation, sum, max, min, and product rule at multilevel stages, these features are used to train deep learning neural network model. In the proposed work, multi-level feature fusion for multimodal biometrics with a deep learning classifier is used and results are analyzed by a varying number of hidden neurons and hidden layers. Experiments are carried out on SDUMLA-HMT, machine learning and data mining lab, Shandong University fingerprint datasets, and MCYT signature biometric recognition group datasets, and encouraging results were obtained.

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“…Reference [23] proposed a new model based on the pulse coupled neural network (PCNN) to enhance finger vein image quality and further to improve the reliability of image recognition. Reference [24] applied CNN to finger vein recognition and achieved better performance than traditional algorithms. However, their research topics are based on the unpolluted finger vein images.…”

Section: Introductionmentioning

confidence: 99%

Finger Vein Image Inpainting With Gabor Texture Constraints

et al. 2020

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The texture edge continuity of a finger vein image is very important for the accuracy of feature extraction. However, the traditional inpainting methods which, without accurate texture constraints, are easy to cause the vein texture of the inpainted image to be blurred and break. A finger vein image inpainting method with Gabor texture constraints is proposed. The proposed method effectively protects the texture edge continuity of the inpainted image. Firstly, using the proposed vertical phase difference coding method, the Gabor texture feature matrix of the finger vein image, which can accurately describe the texture information, can be extracted from the Gabor filtering responses. Then, according to the local texture continuity of the finger vein image, the known pixels, which have different texture orientations with the center pixel in the patch, are filtered out using the Gabor texture constraining mechanism during the inpainting process. The proposed method eliminates irrelevant information interference in the inpainting process and has a more precise texture propagation. Simulation experiments of artificially synthetic images and acquired images show that the finger vein images inpainted by the proposed method have better texture continuity and higher image quality than the traditional methods which do not have accurate texture constraints. The proposed method improves the recognition performance of the finger vein identification system with the acquired damaged images.INDEX TERMS Finger vein image, Gabor filter, image inpainting, texture feature, vertical phase difference coding.HUAXIA WANG (Member, IEEE) received the B.Eng. degree in information engineering from

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Finger vein recognition based on deep learning

Cited by 66 publications

References 17 publications

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

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

Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learning techniques

Finger Vein Image Inpainting With Gabor Texture Constraints

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