A realtime, memory efficient fingerprint verification system

Yang, Shenglin; Verbauwhede, Ingrid

doi:10.1109/icassp.2004.1327079

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Our work has achieved good performance among them. Compared with iris [47,48] or fingerprint [49][50][51], palmprint images have a large area containing rich information and can be easily collected. Furthermore, texture features are much more stable.…”

Section: Comparison With Other Biometricsmentioning

confidence: 99%

Palmprint gender classification by convolutional neural network

Xie

Guo

Qian

2018

IET Computer Vision

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Palmprint gender classification can revolutionise the performance of authentication systems, reduce searching space and speed up matching rate. However, to the best of their knowledge, there is no literature addressing this issue. The authors design a new convolutional neural network (CNN) structure, fine-tuning Visual Geometry Group Network, up to 19 layers to achieve a 20-layer network, for palmprint gender classification. Experimental results show that the proposed structure could achieve good performance for gender classification. They also investigate palmprint images with 15 different kinds of spectra. They empirically find that a palmprint image acquired by the Blue spectrum could achieve 89.2% correct classification and could be considered as a suitable spectrum for gender classification. The neural network is able to classify a 224 × 224 × 3-pixel palmprint image in <23 ms, verifying that the proposed CNN is an effective real-time solution.

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Section: Comparison With Other Biometricsmentioning

confidence: 99%

Palmprint gender classification by convolutional neural network

Xie

Guo

Qian

2018

IET Computer Vision

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“…It describes a better and effic ient embedded fingerprint verification system for the -Thu mbPod‖ embedded device. The ThumbPod is a co mplete real-t ime fingerprint recognition module, which includes minutiae ext raction and the matching [102] .…”

Section: Devices Used For Fingerprint Recognitionmentioning

confidence: 99%

Fingerprints, Iris and DNA Features based Multimodal Systems: A Review

Srivastava¹,

Agrawal²,

Mishra³

et al. 2013

IJITCS

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-Bio met ric systems are alternates to the traditional identification systems. This paper p rovides an overview of single feature and mult iple features based biometric systems, including the performance of physiological characteristics (such as fingerprint, hand geometry, head recognition, iris, retina, face recognition, DNA recognition, palm prints, heartbeat, finger veins, palates etc) and behavioral characteristics (such as body language, facial exp ression, signature verification, speech recognition, Gait Signature etc.).The fingerprints, iris image, and DNA features based mu ltimodal systems and their perfo rmances are analyzed in terms of security, reliab ility, accuracy, and long-term stability. The strengths and weaknesses of various mult iple features based biometric approaches published so far are analyzed. The d irect ions of future research work for robust personal identification is outlined.

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An embedded fingerprint authentication system with reduced hardware resources requirement

Chen

Dai

Proceedings of the Ninth International Symposium on Consumer Electronics, 2005. (ISCE 2005).

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A realtime, memory efficient fingerprint verification system

Cited by 5 publications

References 8 publications

Palmprint gender classification by convolutional neural network

Palmprint gender classification by convolutional neural network

Fingerprints, Iris and DNA Features based Multimodal Systems: A Review

An embedded fingerprint authentication system with reduced hardware resources requirement

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