Finger Vein Recognition Algorithm Based on Lightweight Deep Convolutional Neural Network

Shen, Jiaquan; Liu, Ningzhong; Xu, Chenglu; Sun, Han; Xiao, Yushun; Li, Deguang; Zhang, Yongxin

doi:10.1109/tim.2021.3132332

Cited by 32 publications

(24 citation statements)

References 48 publications

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“…Table 3 shows the results of a comparison with other works [ 6 , 7 , 10 , 11 ], which again verifies that the embedded system proposed in this paper can achieve quite high accuracy. For example, for a well-designed Gabor filter bank proposed by Ref.…”

Section: Resultssupporting

confidence: 69%

“…In Ref. [ 10 ], the paper proposed a lightweight deep-network finger vein recognition algorithm which can effectively extract and match the features of finger vein images and has high recognition accuracy and matching speed. The software method was based on a PC hardware interface and worked on a graphics processing unit (GPU) device for training and inference.…”

Section: Resultsmentioning

confidence: 99%

“…The software method was based on a PC hardware interface and worked on a graphics processing unit (GPU) device for training and inference. The database used SDUMLA-HMT and PKU-FVD [ 10 ]. Compared with the hardware accelerated computing method proposed in this paper, that lightweight deep-network finger vein recognition algorithm needed a high-performance GPU, while our paper has proposed a general, simplified CNN model that could work on an FPGA platform, not needing a GPU, and we also used our own design device to build the database and verified it with SDUMLA-HMT.…”

Section: Resultsmentioning

confidence: 99%

“…Several recent works have proposed lightweight CNN architectures to improve the performance of the recognition task while reducing the computational cost. One such system used a lightweight CNN with triplet loss function, composed of stem blocks for extracting coarse features of the images and stage blocks for extracting detailed features [ 10 ]. Another system proposed a unified CNN which achieved high performance on both finger recognition and anti-spoofing tasks while making their neural network compatible [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

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Design of Low-Complexity Convolutional Neural Network Accelerator for Finger Vein Identification System

Chang

Wang

et al. 2023

Sensors

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In the biometric field, vein identification is a vital process that is constrained by the invisibility of veins as well as other unique features. Moreover, users generally do not wish to have their personal information uploaded to the cloud, so edge computing has become popular for the sake of protecting user privacy. In this paper, we propose a low-complexity and lightweight convolutional neural network (CNN) and we design intellectual property (IP) for shortening the inference time in finger vein recognition. This neural network system can operate independently in client mode. After fetching the user’s finger vein image via a near-infrared (NIR) camera mounted on an embedded system, vein features can be efficiently extracted by vein curving algorithms and user identification can be completed quickly. Better image quality and higher recognition accuracy can be obtained by combining several preprocessing techniques and the modified CNN. Experimental data were collected by the finger vein image capture equipment developed in our laboratory based on the specifications of similar products currently on the market. Extensive experiments demonstrated the practicality and robustness of the proposed finger vein identification system.

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Section: Resultssupporting

confidence: 69%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of Low-Complexity Convolutional Neural Network Accelerator for Finger Vein Identification System

Chang

Wang

et al. 2023

Sensors

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show abstract

“…Convolutional neural networks (CNNs) have a strong characterization capability, but CNNs require a large number of training samples and take a long time to train, which makes it difficult to meet the requirements of real-time detection [29]. To improve this limitation, Shen et al [30] proposed a lightweight convolutional neural network model with a triple loss function to train the model, which can recognize new classes without retraining and achieves extremely high accuracy on both publicly available finger vein datasets. In order to satisfy the real-time finger vein recognition, Kuzu et al [31] proposed a real-time finger vein acquisition system combining convolutional neural network and recurrent neural network, which reduces the recognition time and ensures the recognition accuracy to some extent.…”

Section: A Related Workmentioning

confidence: 99%

An Optimization Algorithm to Improve the Accuracy of Finger Vein Recognition

et al. 2022

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As people's daily behavioral activities become more data-based, how to protect personal information security is a key consideration for the whole society. Finger vein recognition is becoming an important means of identification because of its uniqueness, live detection, security and many other advantages. Although deep learning can make finger vein recognition have good effect, but the number of samples needed to build deep network model is too large, and the current authoritative finger vein database cannot reach the minimum number of samples required. The emergence of Muti-Grained Cascade Forest provides a solution to the problem of insufficient sample data and long training time, which can give a new research avenue in feature extraction. In order to obtain higher accuracy, the deep forest algorithm is introduced in this paper to process the finger vein images. Firstly, the image data in the finger vein image database is pre-processed to prepare for the subsequent feature extraction and matching. Then, the deep forest algorithm is used to find the feature points and ORB algorithm is used to match the features to obtain the angular information of each matched pair, and the final identity is determined according to the sparse distribution of angles. The accuracy of finger vein recognition based on deep forest algorithm is 98.40%. By comparing with other machine learning methods for finger vein recognition, the proposed method has a higher accuracy rate.

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An efficient lightweight convolutional neural network for industrial surface defect detection

et al. 2023

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Finger Vein Recognition Algorithm Based on Lightweight Deep Convolutional Neural Network

Cited by 32 publications

References 48 publications

Design of Low-Complexity Convolutional Neural Network Accelerator for Finger Vein Identification System

Design of Low-Complexity Convolutional Neural Network Accelerator for Finger Vein Identification System

An Optimization Algorithm to Improve the Accuracy of Finger Vein Recognition

An efficient lightweight convolutional neural network for industrial surface defect detection

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