A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Jia, Wei; Gao, Jianliang; Xia, Wei; Min, Hai; Lu, Jingting

doi:10.1007/s11633-020-1257-9

Cited by 42 publications

(16 citation statements)

References 88 publications

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“…Chantaf et al [33] applied Inception-V3 and SmallerVGGNet for palm vein recognition. Stanuch et al [34] proposed a contact-free multispectral palm vein recognition system using a designed CNN, whose architecture comprises of ten different layers, including five convolutional, four max pooling, and one dense layers.…”

Section: D and 3d Palmprint Recognition And Palm Vein Recognition Methods Based On Deep Learningmentioning

confidence: 99%

2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

Jia

Xia

Min

et al. 2021

Int. J. Autom. Comput.

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Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition and have achieved impressive results. In recent years, in the field of artificial intelligence, deep learning has gradually become the mainstream recognition technology because of its excellent recognition performance. Some researchers have tried to use convolutional neural networks (CNNs) for palmprint recognition and palm vein recognition. However, the architectures of these CNNs have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In order to overcome some shortcomings of manually designed CNN, neural architecture search (NAS) technology has become an important research direction of deep learning. The significance of NAS is to solve the deep learning model’s parameter adjustment problem, which is a cross-study combining optimization and machine learning. NAS technology represents the future development direction of deep learning. However, up to now, NAS technology has not been well studied for palmprint recognition and palm vein recognition. In this paper, in order to investigate the problem of NAS-based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct a performance evaluation of twenty representative NAS methods on five 2D palmprint databases, two palm vein databases, and one 3D palmprint database. Experimental results show that some NAS methods can achieve promising recognition results. Remarkably, among different evaluated NAS methods, ProxylessNAS achieves the best recognition performance.

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Section: D and 3d Palmprint Recognition And Palm Vein Recognition Methods Based On Deep Learningmentioning

confidence: 99%

2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

Jia

Xia

Min

et al. 2021

Int. J. Autom. Comput.

Self Cite

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“…Besides, it is important to note that the dataset partitions (i.e. training, validation, and testing) are not standardized, even among the existing approaches themselves [86], which limits the comparison of the proposed approaches.…”

Section: Publicly Available Datasets On Palm Vein Imagesmentioning

confidence: 99%

Towards the Generation of Synthetic Images of Palm Vein Patterns: A Review

Salazar-Jurado,

Hernández-García,

Vilches-Ponce

et al. 2022

Preprint

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With the recent success of computer vision and deep learning, remarkable progress has been achieved on automatic personal recognition using vein biometrics. However, collecting large-scale real-world training data for palm vein recognition has turned out to be challenging, mainly due to the noise and irregular variations included at the time of acquisition. Meanwhile, existing palm vein recognition datasets are usually collected under near-infrared light, lacking detailed annotations on attributes (e.g., pose), so the influences of different attributes on vein recognition have been poorly investigated. Therefore, this paper examines the suitability of synthetic vein images generated to compensate for the urgent lack of publicly available large-scale datasets. Firstly, we present an overview of recent research progress of palm vein recognition, from the basic background knowledge to vein anatomical structure, data acquisition, public database, and quality assessment procedures. Then, we focus on the state-of-the-art methods that have allowed the generation of vascular structures for biometric purposes and the modeling of biological networks with their respective application domains. In addition, we review the existing research on the generation of style transfer and biological nature-based synthetic palm vein images algorithms. Afterward, we formalize a general flowchart for the creation of a synthetic database comparing real palm vein images and generated synthetic samples to obtain some understanding into the development of the realistic vein imaging system. Ultimately, we conclude by

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“…Jia et al [13] evaluate the performance of the deep learning employing 2D and 3D palmprint and palm vein identification , Jia evaluate the performance of the classical convolutional neural networks (CNNs) employing 3D, 2D palm print and palm vein databases, respectively . A technique for identifying palm veins that integrates two different methods utilizing a decision-level fusion mechanism is proposed by Felix et al [11].…”

Section: Vein Recognitionmentioning

confidence: 99%

A Novel Feature Selection Method Based on Bio-inspired Artificial Intelligence for The Feature Selection of Bio-metric Data

Ghoualmi

Benkechakche

Draa

2022

Preprint

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Recently, both industry and academics are paying increasingly more attention to bio-metric authentication using palm vein patterns due to the benefits it provides over traditional methods, like those that rely on a fingerprint, iris, or face. However, feature extraction of the palm vein feature, which seeks to collect the most distinctive features that uniquely identify individuals might have redundant and useless features, which would reduce the performance of the bio-metric system and cause a dimension issue. This paper presents an improved feature selection technique based on a bio-inspired artificial intelligence algorithm called Discrete Artificial Bee Colony (DABC) for palm vein bio-metric authen-tication. The best set of features from those offered by Gabor feature extractor are selected using the DABC algorithm. In attempt to grant additional diversity and effectively explore the solution space, a variant of Opposition Based Learning (OBL) notion is implemented. The proposed approach, termed Modified Opposition based DABC (MODABC), has been evaluated using the CASIA palm vein database in comparison to a number of feature selection algorithms based on bio-inspired artificial intelligence. The experimental findings demonstrate that the presented method perform better that the literature in a number of areas, including accuracy, FRR, FAR, feature vector size, and fitness values. The method being proposed enables providing a suitable balance between the feature vector size and the authentication rate.

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A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Cited by 42 publications

References 88 publications

2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

Towards the Generation of Synthetic Images of Palm Vein Patterns: A Review

A Novel Feature Selection Method Based on Bio-inspired Artificial Intelligence for The Feature Selection of Bio-metric Data

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