Dorsal Hand Vein-Biometric Recognition Using Convolution Neural Network

Kumar, Rajendra; Singh, Ram Chandra; Kant, Shri

doi:10.1007/978-981-15-5113-0_92

Cited by 14 publications

(11 citation statements)

References 30 publications

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“…This learner produces comparable model performance as Adaptive Moment Estimation (Adam) and Root Mean Square Propagation (RMSProp) using a shorter computing time [30]. Our findings (for cropped dataset) are better than many reported works in [7] and [23], but we lose out on classification performance to [25]. This is likely due to the high image quality used in [25].…”

Section: Discussionmentioning

confidence: 47%

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Dorsal Hand Vein Identification using Transfer Learning from AlexNet

Laghari

Tay

Huong

et al. 2022

IJIE

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Dorsal hand vein pattern is a highly secured biometric system that has been used in many applications due to its non-contact attributes. Prior studies focused on investigation of different deep networks for hand vein classification task using different training parameters. It is the aim of this study to propose the use of systematic fine-tuning system for identifying the best parameters value for enhanced model learning efficiency. In this study, pre-trained AlexNet was trained using Bosphorus hand vein database for identification of 100 users. The experiments were carried out using original images, and preprocessed (cropped) images for comparison. The testing accuracies of these datasets were compared following tuning of training parameters, namely trainingand testing split ratio, number of epochs, mini-batch size and initial learning rate. It was observed that the testing accuracy of the model trained using cropped images is higher than that using the original images. The model from preprocessed dataset shows a good testing accuracy of 96 % using a split ratio of 90:10, epoch 50, mini-batch-size of 10 and an initial learning rate of 0.0001. The performance of our trained model is more superior than many reported results in the field. In future, the performance of this classification system may be further enhanced with automatic search of parameters for improved model training efficiency.

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Section: Discussionmentioning

confidence: 47%

“…Kumar [25] captured the dorsal hand vein images using market available NIR camera (VF620). They obtained 99.60 %, 98.46 % and 97.99 % accuracies for good, medium and low-quality images, respectively, using VGG Net-16 and a split ratio 50:50 for training and testing processes.…”

Section: Dorsal Hand Vein Identification Using Transfer Learning From...mentioning

confidence: 99%

Dorsal Hand Vein Identification using Transfer Learning from AlexNet

Laghari

Tay

Huong

et al. 2022

IJIE

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“…Kumar [18] proposed a dorsal hand vein recognition using a CNN approach that used VGG Net-16 to fine-tune good-, medium-, and low-quality images. The images of left and right hands were included in all datasets.…”

Section: Related Workmentioning

confidence: 99%

“…Pooling Layer is between the convolution layers used to decrease the size of the image which is independent of the dimension and the depth where the depth of the image keeps constant. It also reduces the resolution of the features and generates features that are not affected by distortion and noise [18].…”

Section: Fig 2 the General Structure Of A Cnn [15]mentioning

confidence: 99%

Dorsal hand Vein Authentication System Using Convolution Neural Network

Lai¹,

Tay²,

Huong³

et al. 2022

IJETAE

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This study proposes using a dorsal hand vein authentication system using transfer learning from convolutional neural network models of VGG16 and VGG19. The required images were obtained from Bosphorus Hand Vein Database. Among the 100 users, the first 80 users were treated as registered users, while the remaining users as unregistered users. 960 left-hand images of the registered users were trained during the training phase. Meanwhile, 100 images, consisting of 80 registered and 20 unregistered users, were randomly selected for the authentication application testing. Our results showed that VGG19 produced a superior validation accuracy as compared to that of VGG16 given by 96.9% and 94.3%, respectively. The testing accuracy of VGG16 and VGG19 is 99% and 100%, respectively. Since VGG19 is shown to outperform its shallower counterpart, we implemented a User Interface (UI) based on VGG19 model for dorsal hand vein identification. These findings indicate that our system may be deployed for biometric authentication in the future for a more efficient and secure implementation of person identification and imposter detection

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“…Kumar et al [33] demonstrated a new method to confirm persons using the concurrent abstraction of knuckle shape figures. Kumar et al, in another study [34] introduced hand vein patterns by a convolution neural network, the evaluation of consequences suggested prototypical model is complete with other CNN models like VGG-19. The marks of the experiments display that the suggested prototype (finetune of VGG16) attains advanced accuracy of recognition.…”

Section: Related Workmentioning

confidence: 99%