An Improved Recognition Approach for Noisy Multispectral Palmprint by Robust L2 Sparse Representation with a Tensor-Based Extreme Learning Machine

Cheng, Dongxu; Zhang, Xinman; Xu, Xuebin

doi:10.3390/s19020235

Cited by 8 publications

(3 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers have also actively studied palmprint recognition [15]- [25]. Connie et al segmented a palmprint image in the background using a preprocessing module that automatically aligned the palmprint image and extracted the central ROI of the palm for recognition.…”

Section: Related Workmentioning

confidence: 99%

“…Kumar et al proposed an approach for matching contactless palmprint images using accurate deformation alignment and matching [24]. Cheng et al proposed a robust L2 sparse representation with tensor-based extreme learning machine (RL2SR-TELM) algorithm by using an adaptive image level fusion strategy to accomplish the multispectral palmprint recognition [25].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Deep Residual Network-Based Recognition of Finger Wrinkles Using Smartphone Camera

2019

View full text Add to dashboard Cite

Iris, fingerprint, and three-dimensional face recognition technologies used in mobile devices face obstacles owing to price and size restrictions by additional cameras, lighting, and sensors. As an alternative, two-dimensional face recognition based on the built-in visible-light camera of mobile devices has been widely used. However, face recognition performance is greatly influenced by the factors, such as facial expression, illumination, and pose changes. Considering these limitations, researchers have studied palmprint, touchless fingerprint, and finger-knuckle-print recognition using the built-in visible light camera. However, these techniques reduce user convenience because of the difficulty in positioning a palm or fingers on the camera. To consider these issues, we propose a biometric system based on a finger-wrinkle image acquired by the visible-light camera of a smartphone. A deep residual network is used to address the degradation of recognition performance caused by misalignment and illumination variation occurring during image acquisition. Owing to the unavailability of the finger-wrinkle open database obtained by smartphone camera, we built the Dongguk finger-wrinkle database, including the images from 33 people. The results show that the recognition performance by our method exceeds in those of conventional methods. INDEX TERMS Biometrics, finger-wrinkle recognition, smartphone camera, deep residual network.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Deep Residual Network-Based Recognition of Finger Wrinkles Using Smartphone Camera

2019

View full text Add to dashboard Cite

show abstract

“…As a result, these biometric systems have predicted their reliability and robustness against spoofing. This may be due to the fact that this modality presents several information such as, ridges, main and fine lines [15]. Furthermore, vein features that are hidden can be easily detected with infrared light [16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Improving Palmprint based Biometric System Performance using Novel Multispectral Image Fusion Scheme

Thamri¹,

Aloui²,

Naceur³

2020

IJACSA

View full text Add to dashboard Cite

Nowadays, there are several identification systems which are based on different biometric modalities. In particular, multispectral images of palmprints captured in different spectral bands have a very distinctive biometric identifier. This paper proposes a novel fusion scheme of a biometric recognition system by multiSpectral palmprint. This system is composed of three blocks: (1) extraction of the region of interest (ROI) from multispectral images, (2) a new image fusion architecture based on the measurement of decorrelation, and (3) a scheme of dimension reduction and classification. The proposed image fusion system combines the information from the same left and right spectral band using the 2D discrete wavelet (DWT) transform technique. In addition, a feature extraction using the Log-Gabor transform is performed, while the feature size has been reduced using the Kernel Principal Component Analysis technique (KPCA). In Our experiments we use CASIA multispectral palmprint database. We obtained an accuracy rate (ACC) of 99.50% for the spectral bands WHT (white light) and 940 nm and an equal error rate EER = 0.05%.These results show that our system is robust against spoofing.

show abstract

An improved extreme learning machine algorithm for transient electromagnetic nonlinear inversion

Zhang

Gao

et al. 2021

Computers & Geosciences

View full text Add to dashboard Cite

An Improved Recognition Approach for Noisy Multispectral Palmprint by Robust L2 Sparse Representation with a Tensor-Based Extreme Learning Machine

Cited by 8 publications

References 52 publications

Deep Residual Network-Based Recognition of Finger Wrinkles Using Smartphone Camera

Deep Residual Network-Based Recognition of Finger Wrinkles Using Smartphone Camera

Improving Palmprint based Biometric System Performance using Novel Multispectral Image Fusion Scheme

An improved extreme learning machine algorithm for transient electromagnetic nonlinear inversion

Contact Info

Product

Resources

About