Comparative analysis of Palm-Vein recognition system using basic transforms

Vaid, Sonali; Mishra, Durgesh Kumar

doi:10.1109/iadcc.2015.7154875

Cited by 6 publications

(1 citation statement)

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“…Based on the performance of biometrics-based verification systems (Seshikala et al, 2012;Vaid and Mishra, 2015), the false acceptance rate (FAR) and the false rejection rate (FRR) are the primary metrics used to evaluate the performance of the model in this paper. Among them, there are the four categories of True Positive (TP), False Positive (FP), False…”

Section: Algorithm Resultsmentioning

confidence: 99%

Palm Vein Recognition Based on Convolutional Neural Network

Fanjiang¹,

Lee²,

Du³

et al. 2021

Informatica

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Convolutional neural networks (CNNs) were popular in ImageNet large scale visual recognition competition (ILSVRC 2012) because of their identification ability and computational efficiency. This paper proposes a palm vein recognition method based on CNN. The four main steps of palm vein recognition are image acquisition, image preprocessing, feature extraction, and matching. To reduce the processing steps in the recognition of palm vein images, a palm vein recognition method using a CNN is proposed. CNN is a deep learning network. Palm vein images are acquired using near-infrared light, under which the veins in the palm of the hand are relatively prominent. To obtain a good vein image, many previous methods used preprocessing to further enhance the image before using feature extraction to find feature matches for further comparison. In recent years, CNNs have been shown to have great advantages and have performed well in image classification. To reduce early-stage image processing, a CNN is used to classify and recognize palm vein images. The networks AlexNet and VGG depth CNN were trained to extract image features. The palm vein recognition rates by VGG-19, VGG-16, and AlexNet were 98.5%, 97.5%, and 96%, respectively.

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Section: Algorithm Resultsmentioning

confidence: 99%