TY - GEN
T1 - Computational Wrist-Print Biometric Identification System Using Discrete Cosine Transform
AU - Okafor, Kennedy Chinedu
AU - Longe, Omowunmi Mary
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Biometric Wrist Authentication (BWA) is one of the best-known authentication schemes in many access control systems. The use of fingerprint biometrics as humans attempt to communicate with robots/machines, and their physical environments have inherent setbacks. However, various efforts have been proposed to fix the limitations. Most biometric efforts suffer from lack of computational derivatives and do not support optimal image compression. Motivated by these concerns, the goal of this paper is fivefold. First, we proposed BWA using Discrete Cosine Transform (DCT) to compress palm print images and develop Wrist-Print Biometric Identification System (WPBIS). Second, we developed a process model for DCT and characterized it for wrist templates considering both original and decoded images. Third, Bits per pixel (Bpp) and Compression ratio (Cr) for a wrist template/bioscript are used as metrics for evaluation. Fourth, after adopting various timestamps, we observed that the image template Bpp yielded 1.256 Bpp and compression of 63.26% based on DCT. Fifth, we showed a typical experimental scenario with a digital signal processor feeding images with DCT. Identification and verification of various wrist-prints (test-point samples) are equally carried out. From the results, WPBIS DCT offered higher image intensity compared with Wavelet transform.
AB - Biometric Wrist Authentication (BWA) is one of the best-known authentication schemes in many access control systems. The use of fingerprint biometrics as humans attempt to communicate with robots/machines, and their physical environments have inherent setbacks. However, various efforts have been proposed to fix the limitations. Most biometric efforts suffer from lack of computational derivatives and do not support optimal image compression. Motivated by these concerns, the goal of this paper is fivefold. First, we proposed BWA using Discrete Cosine Transform (DCT) to compress palm print images and develop Wrist-Print Biometric Identification System (WPBIS). Second, we developed a process model for DCT and characterized it for wrist templates considering both original and decoded images. Third, Bits per pixel (Bpp) and Compression ratio (Cr) for a wrist template/bioscript are used as metrics for evaluation. Fourth, after adopting various timestamps, we observed that the image template Bpp yielded 1.256 Bpp and compression of 63.26% based on DCT. Fifth, we showed a typical experimental scenario with a digital signal processor feeding images with DCT. Identification and verification of various wrist-prints (test-point samples) are equally carried out. From the results, WPBIS DCT offered higher image intensity compared with Wavelet transform.
KW - Biometric access control
KW - Computational science
KW - Digital signal processor
KW - Human-Computer Interface (HCI)
UR - http://www.scopus.com/inward/record.url?scp=85116071372&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87007-2_33
DO - 10.1007/978-3-030-87007-2_33
M3 - Conference contribution
AN - SCOPUS:85116071372
SN - 9783030870065
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 460
EP - 475
BT - Computational Science and Its Applications – ICCSA 2021 - 21st International Conference, Proceedings
A2 - Gervasi, Osvaldo
A2 - Murgante, Beniamino
A2 - Misra, Sanjay
A2 - Garau, Chiara
A2 - Blečić, Ivan
A2 - Taniar, David
A2 - Apduhan, Bernady O.
A2 - Rocha, Ana Maria
A2 - Tarantino, Eufemia
A2 - Torre, Carmelo Maria
PB - Springer Science and Business Media Deutschland GmbH
T2 - 21st International Conference on Computational Science and Its Applications, ICCSA 2021
Y2 - 13 September 2021 through 16 September 2021
ER -