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Journal articles on the topic 'SDUMLA-HMT'

Author: Grafiati

Published: 11 September 2023

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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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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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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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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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11

K M, Prof Ramya, Pavan H, Darshan Gowda, Bhagavantray Hosamani, and Jagadeva A S. "MULTIMODAL BIOMETRIC IDENTIFICATION SYSTEM USING THE FUSION OF FINGERPRINT AND IRIS RECOGNITION WITH CNN APPROACH." International Journal of Engineering Applied Sciences and Technology 6, no. 8 (2021): 213–20. http://dx.doi.org/10.33564/ijeast.2021.v06i08.036.

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Multimodal biometric systems are widely applied in many real-world applications because of its ability to accommodate variety of great limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, nonuniversality, and vulnerability to spoofing. during this paper, an efficient and real-time multimodal biometric system is proposed supported building deep learning representations for images of both the correct and left irises of someone, and fusing the results obtained employing a ranking-level fusion method. The trained deep learning sys

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12

Feng, Dingzhong, Shanyu He, Zihao Zhou, and Ye Zhang. "A Finger Vein Feature Extraction Method Incorporating Principal Component Analysis and Locality Preserving Projections." Sensors 22, no. 10 (2022): 3691. http://dx.doi.org/10.3390/s22103691.

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In the field of biometric recognition, finger vein recognition has received widespread attention by virtue of its advantages, such as biopsy, which is not easy to be stolen. However, due to the limitation of acquisition conditions such as noise and illumination, as well as the limitation of computational resources, the discriminative features are not comprehensive enough when performing finger vein image feature extraction. It will lead to such a result that the accuracy of image recognition cannot meet the needs of large numbers of users and high security. Therefore, this paper proposes a nov

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Jumaa, Shereen S., and Khamis A. Zidan. "HIGH ACCURACY RECOGNITION BIO METRICS BASED ON FINGER VEIN SCREENING SENSOR." Iraqi Journal of Information & Communications Technology 3, no. 2 (2020): 35–46. http://dx.doi.org/10.31987/ijict.3.2.101.

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One of the safest biometrics of today is finger vein- but this technic arises with some specific challenges, the most common one being that the vein pattern is hard to extract because finger vein images are always low in quality, significantly hampered the feature extraction and classification stages. Professional algorithms want to be considered with the conventional hardware for capturing finger-vein images is by using red Surface Mounted Diode (SMD) leds for this aim. For capturing images, Canon 750D camera with micro lens is used. For high quality images the integrated micro lens is used,

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14

Zhi-Yong Tao, Zhi-Yong Tao, Meng Wang Zhi-Yong Tao, Xin-Ru Zhou Meng Wang, Jie Li Xin-Ru Zhou, and Sen Lin Jie Li. "FFV-MBC: A Novel Fused Finger-Vein Recognition Method Based on Monogenic Binary Coding." 電腦學刊 34, no. 1 (2023): 013–27. http://dx.doi.org/10.53106/199115992023023401002.

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<p>To improve pattern representation capabilities and robustness in traditional finger-vein recognition algorithms. In this paper, we propose FFV-MBC, a novel fused finger-vein recognition method based on monogenic binary coding (MBC). First of all, the amplitude, orientation, and phase information of the finger-vein images are filtered by a multi-scale monogenic log-Gabor filter and encoded by the binary coding theory. Three local features, MBC-A, MBC-P, and MBC-O, are achieved from different combinations of local image intensity and variation coding. After obtaining the features, we ut

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Kamlaskar, Chetana, and Aditya Abhyankar. "Multilinear principal component analysis for iris biometric system." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 3 (2021): 1458. http://dx.doi.org/10.11591/ijeecs.v23.i3.pp1458-1469.

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<p>Iris biometric modality possesses inherent characteristics which make the iris recognition system highly reliable and noninvasive. Nowadays, research in this area is challenging compact template size and fast verification algorithms. Special efforts have been employed to minimize the size of the extracted features without degrading the performance of the iris recognition system. In response, we propose an improved feature fusion approach based on multilinear subspace learning to analyze Iris recognition. This approach consists of four stages. In the first stage, the eye image is segme

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16

Choi, Jiho, Jin Seong Hong, Muhammad Owais, Seung Gu Kim, and Kang Ryoung Park. "Restoration of Motion Blurred Image by Modified DeblurGAN for Enhancing the Accuracies of Finger-Vein Recognition." Sensors 21, no. 14 (2021): 4635. http://dx.doi.org/10.3390/s21144635.

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Among many available biometrics identification methods, finger-vein recognition has an advantage that is difficult to counterfeit, as finger veins are located under the skin, and high user convenience as a non-invasive image capturing device is used for recognition. However, blurring can occur when acquiring finger-vein images, and such blur can be mainly categorized into three types. First, skin scattering blur due to light scattering in the skin layer; second, optical blur occurs due to lens focus mismatching; and third, motion blur exists due to finger movements. Blurred images generated in

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17

Cherrat, El mehdi, Rachid Alaoui, and Hassane Bouzahir. "Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images." PeerJ Computer Science 6 (January 6, 2020): e248. http://dx.doi.org/10.7717/peerj-cs.248.

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In recent years, the need for security of personal data is becoming progressively important. In this regard, the identification system based on fusion of multibiometric is most recommended for significantly improving and achieving the high performance accuracy. The main purpose of this paper is to propose a hybrid system of combining the effect of tree efficient models: Convolutional neural network (CNN), Softmax and Random forest (RF) classifier based on multi-biometric fingerprint, finger-vein and face identification system. In conventional fingerprint system, image pre-processed is applied

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18

Kim, Wan, Jong Min Song, and Kang Ryoung Park. "Multimodal Biometric Recognition Based on Convolutional Neural Network by the Fusion of Finger-Vein and Finger Shape Using Near-Infrared (NIR) Camera Sensor." Sensors 18, no. 7 (2018): 2296. http://dx.doi.org/10.3390/s18072296.

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Finger-vein recognition, which is one of the conventional biometrics, hinders fake attacks, is cheaper, and it features a higher level of user-convenience than other biometrics because it uses miniaturized devices. However, the recognition performance of finger-vein recognition methods may decrease due to a variety of factors, such as image misalignment that is caused by finger position changes during image acquisition or illumination variation caused by non-uniform near-infrared (NIR) light. To solve such problems, multimodal biometric systems that are able to simultaneously recognize both fi

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19

Salama AbdELminaam, Diaa, Abdulrhman M. Almansori, Mohamed Taha, and Elsayed Badr. "A deep facial recognition system using computational intelligent algorithms." PLOS ONE 15, no. 12 (2020): e0242269. http://dx.doi.org/10.1371/journal.pone.0242269.

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The development of biometric applications, such as facial recognition (FR), has recently become important in smart cities. Many scientists and engineers around the world have focused on establishing increasingly robust and accurate algorithms and methods for these types of systems and their applications in everyday life. FR is developing technology with multiple real-time applications. The goal of this paper is to develop a complete FR system using transfer learning in fog computing and cloud computing. The developed system uses deep convolutional neural networks (DCNN) because of the dominant

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20

Kamlaskar, Chetana, and Aditya Abhyankar. "Iris-Fingerprint multimodal biometric system based on optimal feature level fusion model." AIMS Electronics and Electrical Engineering 5, no. 4 (2021): 229–50. http://dx.doi.org/10.3934/electreng.2021013.

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<abstract><p>For reliable and accurate multimodal biometric based person verification, demands an effective discriminant feature representation and fusion of the extracted relevant information across multiple biometric modalities. In this paper, we propose feature level fusion by adopting the concept of canonical correlation analysis (CCA) to fuse Iris and Fingerprint feature sets of the same person. The uniqueness of this approach is that it extracts maximized correlated features from feature sets of both modalities as effective discriminant information within the features sets. C

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21

Teng, Jackson Horlick, Thian Song Ong, Tee Connie, Kalaiarasi Sonai Muthu Anbananthen, and Pa Pa Min. "Optimized Score Level Fusion for Multi-Instance Finger Vein Recognition." Algorithms 15, no. 5 (2022): 161. http://dx.doi.org/10.3390/a15050161.

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The finger vein recognition system uses blood vessels inside the finger of an individual for identity verification. The public is in favor of a finger vein recognition system over conventional passwords or ID cards as the biometric technology is harder to forge, misplace, and share. In this study, the histogram of oriented gradients (HOG) features, which are robust against changes in illumination and position, are extracted from the finger vein for personal recognition. To further increase the amount of information that can be used for recognition, different instances of the finger vein, rangi

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22

Alay, Nada, and Heyam H. Al-Baity. "Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits." Sensors 20, no. 19 (2020): 5523. http://dx.doi.org/10.3390/s20195523.

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With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is b

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23

Mustafa, Ahmed A., and Ahmed AK Tahir. "Improving the Performance of Finger-Vein Recognition System Using A New Scheme of Modified Preprocessing Methods." Academic Journal of Nawroz University 9, no. 3 (2020): 397. http://dx.doi.org/10.25007/ajnu.v9n3a855.

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This paper aims at improving the performance of finger-vein recognition system using a new scheme of image preprocessing. The new scheme includes three major steps, RGB to Gray conversion, ROI extraction and alignment and ROI enhancement. ROI extraction and alignment includes four major steps. First, finger-vein boundaries are detected using two edge detection masks each of size (4 x 6). Second, the correction for finger rotation is done by calculating the finger base line from the midpoints between the upper and lower boundaries using least square method. Third, ROI is extracted by cropping a

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24

Kovač, Ivan, and Pavol Marák. "Openfinger: Towards a Combination of Discriminative Power of Fingerprints and Finger Vein Patterns in Multimodal Biometric System." Tatra Mountains Mathematical Publications 77, no. 1 (2020): 109–38. http://dx.doi.org/10.2478/tmmp-2020-0012.

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Abstract Multimodal biometric systems are nowadays considered as state of the art subject. Since identity establishment in everyday situations has become very significant and rather difficult, there is a need for reliable means of identification. Multimodal systems establish identity based on more than one biometric trait. Hence one of their most significant advantages is the ability to provide greater recognition accuracy and resistance against the forgery. Many papers have proposed various combinations of biometric traits. However, there is an inferior number of solutions demonstrating the u

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25

Shrey Kekade, Piyush Morey, Mayur Rajput, Sahil Karli, and Priyanka Bendale. "Review Paper on an Authentication System using Siamese Convolutional Neural Networks." International Journal of Advanced Research in Science, Communication and Technology, March 26, 2023, 501–4. http://dx.doi.org/10.48175/ijarsct-8874.

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Due to its distinct advantages, finger vein verification has lately drawn more attention. Focusing on the characteristics of finger vein verification, construct a Siamese structure combining with a modified contrastive loss function for training the above CNN, which effectively improves the network's performance. The experimental findings demonstrate that the lightweight CNN's size shrinks to 1/6th of the pretrained-weights based CNN and that it achieves an equal error rate of 75% in the SDUMLA-HMT dataset, which outperforms cutting-edge techniques and nearly maintains the same performance as

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26

"Impostor Detection Based Finger Veins Applying Machine Learning Methods." Iraqi Journal of Computer, Communication, Control and System Engineering, September 30, 2021, 98–111. http://dx.doi.org/10.33103/uot.ijccce.21.3.9.

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Finger veins are different from other biometric signs; it is a special characteristic of the human body. The challenge for an imposter to explore and comprehend it, since the veins are below the skin, it is impossible to tell which one is, and which one stands out because the person has more than one finger to examine. Impostor recognition based on applying three machine-learning methods will be presented in this article, and then there is a discussion at preprocessing, Linear Discriminant Analysis (LDA) for feature extraction, and k fold cross-validation as an evaluation method. These measure

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27

"Impostor Detection Based Finger Veins Applying Machine Learning Methods." Iraqi Journal of Computer, Communication, Control and System Engineering, September 30, 2021, 98–111. http://dx.doi.org/10.33103/uot.ijccce.21.3.9.

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Finger veins are different from other biometric signs; it is a special characteristic of the human body. The challenge for an imposter to explore and comprehend it, since the veins are below the skin, it is impossible to tell which one is, and which one stands out because the person has more than one finger to examine. Impostor recognition based on applying three machine-learning methods will be presented in this article, and then there is a discussion at preprocessing, Linear Discriminant Analysis (LDA) for feature extraction, and k fold cross-validation as an evaluation method. These measure

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28

Boucetta, Aldjia, and Leila Boussaad. "Biometric Authentication Using Finger-Vein Patterns with Deep-Learning and Discriminant Correlation Analysis." International Journal of Image and Graphics, April 22, 2021, 2250013. http://dx.doi.org/10.1142/s0219467822500139.

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Finger-vein identification, a biometric technology that uses vein patterns in the human finger to identify people. In recent years, it has received increasing attention due to its tremendous advantages compared to fingerprint characteristics. Moreover, Deep-Convolutional Neural Networks (Deep-CNN) appeared to be highly successful for feature extraction in the finger-vein area, and most of the proposed works focus on new Convolutional Neural Network (CNN) models, which require huge databases for training, a solution that may be more practicable in real world applications, is to reuse pretrained

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Kolivand, Hoshang, Kayode Akinlekan Akintoye, Shiva Asadianfam, and Mohd Shafry Rahim. "Improved methods for finger vein identification using composite Median-Wiener filter and hierarchical centroid features extraction." Multimedia Tools and Applications, March 1, 2023. http://dx.doi.org/10.1007/s11042-023-14469-z.

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AbstractFinger vein patterns contain highly discriminative characteristics, which are difficult to be forged due to residing underneath the skin. Several pieces of research have been carried out in this field but there is still an unresolved issue when data capturing and processing is of low quality. Low-quality data have caused errors in the feature extraction process and reduced identification performance rate in finger vein identification. The objective of this paper is to address this issue by presenting two methods, a new image enhancement, and a feature extraction method. The image enhan

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