On Wrist Vein Recognition For Human Biometrics

Marattukalam, Felix; Cole, David; Gulati, Pranav; Abdulla, Waleed H.

doi:10.23919/apsipaasc55919.2022.9980223

Cited by 3 publications

(14 citation statements)

References 45 publications

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“…When compared to traditional feature extraction methods that are reviewed in [ 6 ], deep learning compensates for differences in the position, translation and rotation of the wrist in the image. They are also able to encompass all signal-processing stages.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Both paths consist of 3 × 3 convolutional blocks, ReLU activation functions and 2 × 2 max pooling blocks. In one of our previous works, we applied U-Net to palm vein image for segmentation in [ 32 ]. The U-Net architecture was modified to decrease the filters in the first and final 2 × 2 convolutional blocks and a Gabor filter was introduced to the first convolutional block to help with extracting the palm vein features.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The U-Net CNN was used for image segmentation in this paper. The U-Net architecture was chosen due to its success in segmenting palm vein images in our previous work [ 32 ].…”

Section: Proposed Systemmentioning

confidence: 99%

“…The wrist vein is known to have sufficient unique features to be used in a vascular biometric system [ 6 ]. Wrist vein recognition has seen less research when compared to its palm and finger vein counterparts.…”

Section: Introductionmentioning

confidence: 99%

“…The contributions of this paper are listed below: Extension of the literature survey carried out in our previous study [ 6 ]. Segmentation of wrist vein images using a modified UNet architecture.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Wrist Vascular Biometric Recognition

Marattukalam

Abdulla

Cole

et al. 2023

Sensors

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The need for contactless vascular biometric systems has significantly increased. In recent years, deep learning has proven to be efficient for vein segmentation and matching. Palm and finger vein biometrics are well researched; however, research on wrist vein biometrics is limited. Wrist vein biometrics is promising due to it not having finger or palm patterns on the skin surface making the image acquisition process easier. This paper presents a deep learning-based novel low-cost end-to-end contactless wrist vein biometric recognition system. FYO wrist vein dataset was used to train a novel U-Net CNN structure to extract and segment wrist vein patterns effectively. The extracted images were evaluated to have a Dice Coefficient of 0.723. A CNN and Siamese Neural Network were implemented to match wrist vein images obtaining the highest F1-score of 84.7%. The average matching time is less than 3 s on a Raspberry Pi. All the subsystems were integrated with the help of a designed GUI to form a functional end-to-end deep learning-based wrist biometric recognition system.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…The U-Net CNN was used for image segmentation in this paper. The U-Net architecture was chosen due to its success in segmenting palm vein images in our previous work [ 32 ].…”

Section: Proposed Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Wrist Vascular Biometric Recognition

Marattukalam

Abdulla

Cole

et al. 2023

Sensors

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Explainable Vision Transformers for Vein Biometric Recognition

Albano,

Giusti,

Maiorana

et al. 2024

IEEE Access

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In the field of deep learning, understanding the rationale behind an automatic system's decisions is essential for building users' trust and ensuring accountability. In this regard, explainable artificial intelligence (XAI) recently emerged as a valuable tool to offer insights into a model behavior. The present study focuses on vein-based biometric recognition, investigating techniques allowing to identify which regions of a wrist-vein image are mostly exploited to carry out a verification process. Toward this aim, our research exploits vision transformers (ViTs), which rely on self-attention mechanisms to automatically detect and exploit the input parts with the content deemed most relevant for its further processing. Two distinct wrist-vein pattern datasets, namely PUT-wrist and FYO-wrist, are employed to finetune the considered models. Their behavior is interpreted by analyzing the attention maps generated when applying the trained networks to vein-pattern images, investigating which regions are exploited to decide a user's identity. The proposed approach testifies that the performed recognition process can improve when a ViT focuses on areas with significant vein pattern content, achieving verification performance surpassing state-of-the-art methods in open-set scenarios, while promoting transparency through explainability.

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Subcutaneous Vein Recognition System Using Deep Learning for Intravenous (IV) Access Procedure

Jing,

Meng,

Chai

et al. 2023

IJIE

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Intravenous(IV) access is animportant daily clinical procedure that delivers fluids or medication into a patient’s vein. However, IV insertion is very challenging where clinicians aresuffering in locating the subcutaneous vein due to patients’physiological factorssuch as hairy forearm and thick dermis fat, and alsomedical staff’s level of fatigue. To resolve this issue, researchers have proposed autonomous machines to be usedfor IV access,but such equipmentarelackingcapabilityin detecting the vein accurately. Therefore, this project proposes an automatic veindetection algorithm using deep learning for IVaccess purpose. U-Net, a fully connected network (FCN) architecture is employed in thisproject due to its capability in detecting the near-infrared (NIR)subcutaneous vein. Data augmentation is applied toincrease the dataset size and reduce the bias from overfitting. The original U-Net architecture is optimized by replacing up-sampling with transpose convolution as well as theadditional implementation ofbatch normalizationbesides reducing the number oflayers to diminish the risk of overfitting.After fine-tuning and retraining the hypermodel, an unsupervised dataset is used to evaluate the hypermodel by selecting 10 checkpoints foreach forearm image and comparing the checkpoints onpredicted outputs to determine true positive vein pixels. The proposed lightweight U-Net has achieved slightly lower accuracy (0.8871) than the original U-Net architecture. Even so, the sensitivity, specificity, and precisionare greatly improved byachieving 0.7806, 0.9935,and 0.9918 respectively. This result indicates that the proposed algorithm can be applied into the venipuncture machine toaccurately locate the subcutaneous vein for intravenous (IV)procedures.

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On Wrist Vein Recognition For Human Biometrics

Cited by 3 publications

References 45 publications

Deep Learning-Based Wrist Vascular Biometric Recognition

Deep Learning-Based Wrist Vascular Biometric Recognition

Explainable Vision Transformers for Vein Biometric Recognition

Subcutaneous Vein Recognition System Using Deep Learning for Intravenous (IV) Access Procedure

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