A multiple classifiers-based approach to palmvein identification

Micheletto, Marco; Orrù, Giulia; Rida, Imad; Ghiani, Luca; Marcialis, Gian Luca

doi:10.1109/ipta.2018.8608150

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Subspace-based approaches [28][29][30][31][32][33][34] are also proposed to reduce the dimensionality of training data to a lower-dimensional space. These methods include principal component analysis (PCA), linear discriminative analysis (LDA), Fisher linear discriminant (FLD), and independent component analysis (ICA).…”

Section: Handcrafted Methodsmentioning

confidence: 99%

Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet

Htet

2023

Applied Sciences

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Palm vein recognition has received some considerable attention regarding its use in biometric identification. Palm vein characteristics offer a superior level of security and reliability in personal identification compared to extrinsic methods such as fingerprint, face, and palm print recognition, as vein patterns are difficult to duplicate and do not change throughout one’s lifetime. This study proposes both segmentation and recognition methods to enhance the authentication performance and achieve correct identification using palm vein features. First, we propose a segmentation model based on the U-Net model, enhanced with an attention gate, to effectively segment palm vein patterns. The incorporation of both the attention gate and residual block allows the segmentation model for the learning of the essential features required for specific segmentation tasks. The Hessian-based Jerman filtering method is used for ground-truth labeling. The segmentation model extracts the palm vein patterns and filters out the irrelevant and noisy pixels for the purpose of recognition. The efficient channel attention residual network is trained to learn discriminative features for personal identification using combined margin-based loss functions for palm vein recognition. The channel attention module enhances the useful information and suppresses irrelevant features in the feature maps, which overcomes the problem of rotation, position translation, and scale transformation, as well as improves the recognition rate. The combined loss function used in this study increases the similarity between the intra-class samples and the diversity between inter-class samples. The proposed recognition model achieved 100% accuracy for palm vein recognition and an equal error rate of 0.018 for palm vein verification.

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Section: Handcrafted Methodsmentioning

confidence: 99%

Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet

Htet

2023

Applied Sciences

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“…Subspace-based methods, [17], [18], [19], [20] project palm vein images into subspaces. Subspace approaches, including Principal Component Analysis (PCA), Linear Discriminative Analysis (LDA), Independent Component Analysis (ICA), take the identified object as a whole.…”

Section: A Handcrafted Methodsmentioning

confidence: 99%

Outside Box and Contactless Palm Vein Recognition Based on a Wavelet Denoising ResNet

Wang

et al. 2021

IEEE Access

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Palm vein recognition is a high-security biometric. Outside the NIR capture box and contactless palm vein recognition are more popular but challenging. The users feel comfortable outside the NIR capture box but face more optical blurring brought by visible light. Contactless capture gestures solve the hygienic problem but face the image rotation, position translation, and scale transformation which makes classification difficult especially in large-scale databases. To address these problems, we develop a wavelet denoising ResNet, which consists of two models: the wavelet denoising (WD) model and the squeeze-and-excitation ResNet18 (SER) model. The WD model focuses on removing noise from skin scattering and optical blurring from palm vein images. The WD model enhances the low-frequency feature into a deep learning feature by residual learning technology. This strategy increases the weight of an effective handcrafted feature in the deep learning network. The SER model overcomes rotation, position translation, and scale transformation by selectively emphasizing classification features and weakening less useful features. To train and verify the network, an inside box palm vein image database and an outside box palm vein image database are set up. The Tongji contactless palm vein image database was also employed in the experiments. The validity and superiority of our network are verified in a series of experiments.

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“…Subspace‐based approaches have emerged as a powerful technique, in which the palm vein images are projected into subspaces built from training data. Subspace approaches including principal component analysis (PCA) [62, 63], 2D‐PCA [63, 64], linear discriminative analysis [65, 66], independent component analysis, non‐negative matrix factorisation, 2‐dimensional Fisher linear discriminant [67] and its variant, take subspace coefficients as features without prior knowledge. They take the palm vein image as the whole object.…”

Section: Feature Extraction and Matchingmentioning

confidence: 99%

Review of palm vein recognition

Elliott

Lin

et al. 2019

IET biom.

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As a new research focus in biometrics, palm vein recognition has attracted people's attention because of high security, liveness-detection, user acceptability and convenience. Palm vein recognition exhibits high security, as it uses the network of blood vessels underneath palm skin for recognition. As palm vein is interior biological information of the body, vein patterns are much harder for intruders to copy compared to other biometric features. Palm vein is mostly invisible to the human eyes; they are commonly captured under near-infrared light. Under its natural state, the veins of the palm are concealed naturally and, for most of time people's hands are in the gesture of half fist. This study presents a comprehensive overview of recent research progress of palm vein recognition, from the basic background knowledge to data acquisition, public database, preprocessing, feature extraction and matching. In addition, the study focuses on palm vein-related fusion. Ultimately, the authors discuss the challenges and future perspectives in palm vein recognition for further works.

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A multiple classifiers-based approach to palmvein identification

Cited by 5 publications

References 19 publications

Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet

Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet

Outside Box and Contactless Palm Vein Recognition Based on a Wavelet Denoising ResNet

Review of palm vein recognition

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