Fusion of Low Frequency Coefficients of DCT Transform Image for Face and Palmprint Multimodal Biometrics

Mohamad, Nurain; Isa, Mohd Nazrin Md; Ngadiran, Ruzelita; Darsono, A. M.

doi:10.1109/cybconf.2017.7985778

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Following the acquisition of DNA sequences, it conducts DNA enclosure on the two DNA provisions, a pattern defense technique based on a feature transformation approach for multimodal biometric identification. is approach utilizes the mixture of high points obtained by mixing DCT coefficients from face and palm print pictures [7], a mechanism for merging characteristics at the trait level, and an RSA methodology for securing multiple traits. Real-time biometric modality cryptosystems also received a security assessment due to their fingerprint, finger vein, and retina usage.…”

Section: Related Workmentioning

confidence: 99%

“…(1) Precision: multimodal biometrics stores data from two or more biometric characteristics (e.g., a fingerprint and a face; or a face and an iris), while unimodal biometric arrangements store data from a single biometric characteristic [6,7]. (2) Increased and Reliable Recognition: numerous characteristics are accessible, increasing the reliability of the multimodal system.…”

Section: Introductionmentioning

confidence: 99%

“…(2) Increased and Reliable Recognition: numerous characteristics are accessible, increasing the reliability of the multimodal system. A multimodal biometric method enables greater precision in identifying and verifying processes [6,7]. (3) Enhanced Security: the employment of various documentation and acknowledgment techniques allows an arrangement to identify several biometric characteristics to reach a greater grade of threshold acknowledgment, and a structure administrator may choose the exact security level required [6,7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Authentication System with Privacy Preservation of Biometrics

Patil¹,

Raut²,

Jhaveri

et al. 2022

Security and Communication Networks

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IoT-based multi-biometric system is a blend of multiple biometric templates that can be used for user authentication/verification using sensors. The leakage of the biometric trait information may cause critical privacy and security issues. It is expected to protect the privacy details of individuals through the irreversibility, unlinkability, and renewability of multi-biometric templates used in the authentication system. This study presents a robust authentication system with secure multi-biometric template protection techniques based on discrete cosine transform feature transformation and Lagrange’s interpolation-based image transformation. Three biometric traits namely iris, fingerprint, and palm print are recorded using sensors to validate the proposed multi-biometric template protection system. The fusion of all traits used is giving an average of 95.42% genuine acceptance rate and an average of 4.57% false rejection rate. Despite any number of biometric templates used for authentication, the proposed image transformation techniques keep the size of the final storage requirement as 8 X 8, which achieves constant space complexity (O(1)). The stored template is not linked with original templates; it is irreversible and renewable as new enrolment of the same individual will produce a new template every time. Overall, the proposed technique provides a secure authentication system with high accuracy, a constant size database, and the privacy preservation of biometric traits.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Authentication System with Privacy Preservation of Biometrics

Patil¹,

Raut²,

Jhaveri

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…These days, biometric identification systems are widely used and difficult to use in many places where very high security is required. The system is said to have numerous benefits using biometrics, including unique, strong, and high privilege on its own for personal identity because it is thought that each person's biometric cannot be shared, stolen, or lost Ahmad et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Discrete Shearlet Transform and Lempel-Ziv Welch Coding for Lossless Fingerprint Image Compression

Kadim,

Guirguis,

Elsayed

2024

Journal of Computer Science

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Image compression is a crucial task in image processing and in the process of sending and receiving files. There is a need for effective techniques for image compression as the raw images require large amounts of disk space to defect during transportation and storage operations. The most important objective of image compression is to decrease the redundancy of the image which helps in increasing the storage capacity and then efficient transmission. This study introduces a system for lossless image compression that is built to work on fingerprint image compression. It uses lossless compression to take care of the first image during processing. However, there is a serious problem which is the low ratio of compression. In order to make the ratio higher, there are five lossless compression techniques used in this study which are Elias Gamma Coding (EGC), Huffman Coding (HC), Arithmetic Coding (AC), Run-Length Encoding (RLE) and Lempel Ziv Welch (LZW). With these techniques, there are three types of transforms are used; they are Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Discrete Shearlet Transform (DST). The results conclude that discrete shearlet transform with the Lempel-Ziv Welch coding technique outperforms the other lossless compression techniques and its Compression Ratio (CR) is 3.678023.

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A Bimodal Biometric System Using Palmprint and Face Modality

Harivinod¹,

Shekar

2018

Data Analytics and Learning

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Fusion of Low Frequency Coefficients of DCT Transform Image for Face and Palmprint Multimodal Biometrics

Cited by 6 publications

References 20 publications

Robust Authentication System with Privacy Preservation of Biometrics

Robust Authentication System with Privacy Preservation of Biometrics

Discrete Shearlet Transform and Lempel-Ziv Welch Coding for Lossless Fingerprint Image Compression

A Bimodal Biometric System Using Palmprint and Face Modality

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