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Relevant bibliographies by topics / SDUMLA-HMT

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Journal articles

Academic literature on the topic 'SDUMLA-HMT'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "SDUMLA-HMT"

1

Noh, Kyoung Jun, Jiho Choi, Jin Seong Hong, and Kang Ryoung Park. "Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network." Sensors 21, no. 2 (2021): 524. http://dx.doi.org/10.3390/s21020524.

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The conventional finger-vein recognition system is trained using one type of database and entails the serious problem of performance degradation when tested with different types of databases. This degradation is caused by changes in image characteristics due to variable factors such as position of camera, finger, and lighting. Therefore, each database has varying characteristics despite the same finger-vein modality. However, previous researches on improving the recognition accuracy of unobserved or heterogeneous databases is lacking. To overcome this problem, we propose a method to improve th

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2

Noroz, Noroz Khan Baloch, Saleem Ahmed Ahmed, Ramesh Kumar Kumar, DM Saqib Bhatii Bhatti, and Yawar Rehaman Rehman. "Finger-Vein Image Dual Contrast Adjustment and Recognition Using 2D-CNN." Sukkur IBA Journal of Computing and Mathematical Sciences 6, no. 1 (2022): 16–25. http://dx.doi.org/10.30537/sjcms.v6i1.1001.

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The suggested process enhances the low contrast of the finger-vein image using dual contrast adaptive histogram equalization (DCLAHE) for visual attributes. The finger-vein histogram intensity is split out all over the image when dual CLAHE is used. For preprocessing, the finger-vein image dataset is obtained from the SDUMLA-HMT finger-vein database. Following the deployment of DCLAHE, the updated dataset is used to recognize objects using an improved 2D-CNN model. The 2D CNN model learns features by optimizing values of a preprocessed dataset. The accuracy of this model stands at 91.114%.

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3

Sharif, Hanan, Faisal Rehman, Naveed Riaz, Rana Mohtasham Aftab, Adnan Ashraf, and Azher Mehmood. "Identification of Finger Vein Images with Deep Neural Networks." Lahore Garrison University Research Journal of Computer Science and Information Technology 7, no. 02 (2023): 29–36. http://dx.doi.org/10.54692/lgurjcsit.2023.0702425.

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To establish identification, individuals often utilize biometrics so that their identity cannot be exploited without their consent. Collecting biometric data is getting easier. Existing smartphones and other intelligent technologies can discreetly acquire biometric information. Authentication through finger vein imaging is a biometric identification technique based on a vein pattern visible under finger's skin. Veins are safeguarded by the epidermis and cannot be duplicated. This research focuses on the consistent characteristics of veins in fingers. We collected invariant characteristics from

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4

Li, Jun, Luokun Yang, Mingquan Ye, Yang Su, and Juntong Liu. "Finger Vein Verification on Different Datasets Based on Deep Learning with Triplet Loss." Computational and Mathematical Methods in Medicine 2022 (October 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/4868435.

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In this study, deep learning and triplet loss function methods are used for finger vein verification research, and the model is trained and validated between different kinds of datasets including FV-USM, HKPU, and SDUMLA-HMT datasets. This work gives the accuracy and other evaluation indexes of finger vein verification calculated for different training-validation set combinations and gives the corresponding ROC curves and AUC values. The accuracy of the best result has reached 98%, and all the ROC AUC values are above 0.98, indicating that the obtained model can identify the finger veins well.

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5

Hsia, Chih-Hsien, Zi-Han Yang, Hong-Jyun Wang, and Kuei-Kuei Lai. "A New Enhancement Edge Detection of Finger-Vein Identification for Carputer System." Applied Sciences 12, no. 19 (2022): 10127. http://dx.doi.org/10.3390/app121910127.

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Developments in multimedia and mobile communication technologies and in mobilized, personalized information security has benefitted various sectors of society, as traditional identification technologies are often complicated. In response to the sharing economy and the intellectualization of automotive electronics, major automobile companies are using biometric recognition to enhance the safety, uniqueness, and convenience of their vehicles. This study uses a deep learning-based finger-vein identification system for carputer systems. The proposed enhancement edge detection adapts to the detecte

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6

Ahmed, Mona A., and Abdel-Badeeh M. Salem. "Intelligent Technique for Human Authentication using Fusion of Finger and Dorsal Hand Veins." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 18 (July 9, 2021): 91–101. http://dx.doi.org/10.37394/23209.2021.18.12.

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Multimodal biometric systems have been widely used to achieve high recognition accuracy. This paper presents a new multimodal biometric system using intelligent technique to authenticate human by fusion of finger and dorsal hand veins pattern. We developed an image analysis technique to extract region of interest (ROI) from finger and dorsal hand veins image. After extracting ROI we design a sequence of preprocessing steps to improve finger and dorsal hand veins images using Median filter, Wiener filter and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance vein image. Our sma

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7

Mahmoud, Rasha O., Mazen M. Selim, and Omar A. Muhi. "Fusion Time Reduction of a Feature Level Based Multimodal Biometric Authentication System." International Journal of Sociotechnology and Knowledge Development 12, no. 1 (2020): 67–83. http://dx.doi.org/10.4018/ijskd.2020010104.

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In the present study, a multimodal biometric authentication method is presented to confirm the identity of a person based on his face and iris features. This method depends on multiple biometric techniques that combine face and iris (left and right) features to recognize. The authors have designed and applied a system to identify people. It depends on extracting the features of the face using Rectangle Histogram of Oriented Gradient (R-HOG). The study applies a feature-level fusion using a novel fusion method which employs both the canonical correlation process and the proposed serial concaten

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8

Yulianto, Vandy Achmad, Nazrul Effendy, and Agus Arif. "Finger vein identification system using capsule networks with hyperparameter tuning." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1636. http://dx.doi.org/10.11591/ijai.v12.i4.pp1636-1643.

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<p>Safety and security systems are essential for personnel who need to be protected and valuables. The security and safety system can be supported using a biometric system to identify and verify permitted users or owners. Finger vein is one type of biometric system that has high-level security. The finger vein biometrics system has two primary functions: identification and verification. Safety and security technology development is often followed by hackers' development of science and technology. Therefore, the science and technology of safety and security need to be continuously develop

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9

Sari, Jayanti Yusmah, and Rizal Adi Saputra. "Pengenalan Finger Vein Menggunakan Local Line Binary Pattern dan Learning Vector Quantization." Jurnal ULTIMA Computing 9, no. 2 (2018): 52–57. http://dx.doi.org/10.31937/sk.v9i2.790.

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This research proposes finger vein recognition system using Local Line Binary Pattern (LLBP) method and Learning Vector Quantization (LVQ). LLBP is is the advanced feature extraction method of Local Binary Pattern (LBP) method that uses a combination of binary values from neighborhood pixels to form features of an image. The straight-line shape of LLBP can extract robust features from the images with unclear veins, it is more suitable to capture the pattern of vein in finger vein image. At the recognition stage, LVQ is used as a classification method to improve recognition accuracy, which has

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10

Channegowda, Arjun Benagatte, and H. N. Prakash. "Multimodal biometrics of fingerprint and signature recognition using multi-level feature fusion and deep learning techniques." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 187. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp187-195.

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Providing security in biometrics is the major challenging task in the current situation. A lot of research work is going on in this area. Security can be more tightened by using complex security systems, like by using more than one biometric trait for recognition. In this paper multimodal biometric models are developed to improve the recognition rate of a person. The combination of physiological and behavioral biometrics characteristics is used in this work. Fingerprint and signature biometrics characteristics are used to develop a multimodal recognition system. Histograms of oriented gradient

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