A Study on the Dynamic Binary Fingerprint Recognition Method using Artificial Intelligence

Kang, Jong-Yoon; Lee, Joo-Sang; Lee, Jae‐Hyun; Kong, Suk-Min; Kim, Donghan; Lee, Sang-Bae

doi:10.5391/jkiis.2003.13.1.057

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…For the histogram derived from a fingerprint file, Kang et al [2,3] suggested an efficient preprocessing approach for evaluating a threshold value using neuro-fuzzy method or a neural network. After extracting the ridge information in 16 directions using the Markov model for preprocessed fingerprint images, Jeong and Lee [4] classified them with 88% accuracy while changing the Markov model value using a genetic algorithm.…”

Section: Related Literaturementioning

confidence: 99%

Önceden Eğitilmiş Çeşitli Derin Sinir Ağları Kullanarak Parmak İzi Örüntü Sınıflandırma

Çimtay¹,

Alkan²,

Demirel³

2021

European Journal of Science and Technology

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Biometrics technology is very important in terms of security issues like the identification of personal identity. Many solutions have been offered regarding biometric technology such as eyes-iris recognition, face recognition and vein pattern recognition. Moreover, one of the today's most important authentication methods is fingerprint recognition. Each fingerprint has different pattern of ridges, valleys, deltas and cores. Those pattern types indicate unique fingerprints such as arch, left loop, right loop, tent arch and whorl. The issue of fingerprint pattern recognition is a crucial prior step to speed up the matching process of fingerprint recognition systems. Therefore, an accurate pattern recognition method is always needed, especially for large fingerprint databases. Besides traditional methods, recently, CNN is mostly used for fingerprint pattern recognition and there are many studies in the literature which achieve high recognition rates. In this study, we propose an automated tecnique toward fingerprint classification using various pretrained CNNs Xception and NasNetLarge in order to increase recognition rates. We performed experiments using NIST Special database 4 and we achieved 97.3 98.5% recognition rates respectively, which are the best scores up to now, for four categories: arch, right loop, left loop and whorl. The models was also tested into 5 fingerprint classes which arch and tented arch were seperated as two different classes with the recognition rate of 91.5% and 90.2% respectively.

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Section: Related Literaturementioning

confidence: 99%

Önceden Eğitilmiş Çeşitli Derin Sinir Ağları Kullanarak Parmak İzi Örüntü Sınıflandırma

Çimtay¹,

Alkan²,

Demirel³

2021

European Journal of Science and Technology

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show abstract

“…Kang et al [1,2] proposed an effective preprocessing method for determining a threshold value using a neural network or a neuro-fuzzy method for the histogram extracted from a fingerprint image. Jeong and Lee [3] classified fingerprint images with 88% accuracy while modifying the Markov model value using a genetic algorithm after extracting the ridge information in 16 directions using the Markov model for preprocessed fingerprint images.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Pattern Classification Using Convolution Neural Network

Jeon¹,

Rhee²

2017

IJFIS

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Biometrics technology determines the correct identity of a person by extracting human biological or behavioral characteristic data. As the possibility of hacking increases with the development of IT technology, interest in biometrics and authentication technology is greatly increasing. Currently, the most popular authentication technology is fingerprint recognition. For the sake of efficiency, fingerprint recognition is divided into two stages. In the first step, the inputted fingerprint image is subjected to a complicated preprocessing stage, and the fingerprint image is then classified. In the second step, the feature points of the classified fingerprints are extracted and compared with the fingerprint feature points stored in a database. Human beings can easily classify fingerprint patterns without complicated image processing. In this paper, we propose the use of a convolution neural network model combined with an ensemble model and a batch normalization technique after minimizing the number of the quality improvement processes required for a fingerprint image, which operates more similarly to human perception.

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One Grip based Doorpull Shaped Doorlock System using Fingerprint Recognition and Touch Pattern

Jang¹,

Park²,

Lee³

et al. 2016

Journal of Korean Institute of Intelligent Systems

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A Study on the Dynamic Binary Fingerprint Recognition Method using Artificial Intelligence

Cited by 3 publications

References 1 publication

Önceden Eğitilmiş Çeşitli Derin Sinir Ağları Kullanarak Parmak İzi Örüntü Sınıflandırma

Önceden Eğitilmiş Çeşitli Derin Sinir Ağları Kullanarak Parmak İzi Örüntü Sınıflandırma

Fingerprint Pattern Classification Using Convolution Neural Network

One Grip based Doorpull Shaped Doorlock System using Fingerprint Recognition and Touch Pattern

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