Academic literature on the topic 'SDUMLA-HMT'
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Journal articles on the topic "SDUMLA-HMT"
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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 (January 13, 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 the finger-vein recognition accuracy using domain adaptation between heterogeneous databases using cycle-consistent adversarial networks (CycleGAN), which enhances the recognition accuracy of unobserved data. The experiments were performed with two open databases—Shandong University homologous multi-modal traits finger-vein database (SDUMLA-HMT-DB) and Hong Kong Polytech University finger-image database (HKPolyU-DB). They showed that the equal error rate (EER) of finger-vein recognition was 0.85% in case of training with SDUMLA-HMT-DB and testing with HKPolyU-DB, which had an improvement of 33.1% compared to the second best method. The EER was 3.4% in case of training with HKPolyU-DB and testing with SDUMLA-HMT-DB, which also had an improvement of 4.8% compared to the second best method.
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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 (July 21, 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 (August 21, 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 several cutting-edge deep learning techniques before classifying them using multiclass SVM. We used publicly available image datasets of finger veins for this purpose. Several assessment criteria and a comparison of different deep learning approaches were used to characterize the performance and efficiency of these models on the SDUMLA-HMT dataset.
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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. Since the experiments are cross-validated between different kinds of datasets, the model has good adaptability and applicability. From the experimental results, it is also found that the model trained on the dataset that is more difficult to be distinguished will be a better and more robust model.
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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 (October 9, 2022): 10127. http://dx.doi.org/10.3390/app121910127.
Full textAbstract:
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 detected fingers’ rotational and translational movements and to interference from external light and other environmental factors. This study also determines the effect of preprocessing methods on the system’s effectiveness. The experimental results demonstrate that the proposed system allows more accurate identification of 99.1% and 98.1% in various environments, using the FV-USM and SDUMLA-HMT public datasets. As results, the contribution of system is high accuracy and stability for more sanitary, contactless applications makes it eminently suited for use during the COVID-19 pandemic.
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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 smart technique is based on the following intelligent algorithms, namely; principal component analysis (PCA) algorithm for feature extraction and k-Nearest Neighbors (K-NN) classifier for matching operation. The database chosen was the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT) and Bosphorus Hand Vein Database. The achieved result for the fusion of both biometric traits was Correct Recognition Rate (CRR) is 96.8%.
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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 (January 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 concatenation. A deep belief network was used for the recognition process. The performance of the proposed systems was validated and evaluated through a set of experiments on SDUMLA-HMT database. The results were compared with others, and have shown that the fusion time has been reduced by about 34.5%. The proposed system has also succeeded in achieving a lower equal error rate (EER), and a recognition accuracy up to 99%.
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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 (December 1, 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 developed. The paper proposes finger vein identification using capsule networks with hyperparameter tuning. The augmentation, convolution layer parameters, and capsule layers are optimized. The experimental results show that the capsule network with hyperparameter tuning successfully identifies the finger vein images. The system achieves an accuracy of 91.25% using the Shandong University machine learning and applications-homologous multimodal traits (SDUMLA-HMT) dataset.</p>
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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 (April 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 been shown in earlier studies to show better results than other classifier methods. The three main stages in this research are preprocessing, feature extraction using LLBP method and recognition using LVQ. The proposed methodology has been tested on the SDUMLA-HMT finger vein image database from Shandong University. The experiment shows that the proposed methodology can achieve accuracy up to 90%.
Index Terms—finger vein recognition, Learning Vector Quantization, LLBP, Local Line Binary Pattern, LVQ.
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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 (April 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 gradients (HOG) features are extracted from biometric traits and for these feature fusions are applied at two levels. Features of fingerprint and signatures are fused using concatenation, sum, max, min, and product rule at multilevel stages, these features are used to train deep learning neural network model. In the proposed work, multi-level feature fusion for multimodal biometrics with a deep learning classifier is used and results are analyzed by a varying number of hidden neurons and hidden layers. Experiments are carried out on SDUMLA-HMT, machine learning and data mining lab, Shandong University fingerprint datasets, and MCYT signature biometric recognition group datasets, and encouraging results were obtained.
Dissertations / Theses on the topic "SDUMLA-HMT"
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GUPTA, AMIT. "IMPROVED PALM PRINT RECOGNITION." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15794.
Full textAbstract:
In existing biometric framework finger prints are normally forged; voice, signature, and
hand shapes are easily forged; and biometrics, for example, fingerprints, iris and face
recognition, are susceptible to forge, i.e., the biometric identifiers can be duplicated and
used to make artifacts that can betray numerous presently accessible biometric gadgets.
The biggest challenge to bio-metrics is to improve recognition performance in terms of
accuracy and efficiency both and become maximum resistant to spoofing attacks. To this
end, numerous specialists have tried to enhance unwavering quality and baffle spoofs by
creating biometrics that are exceedingly individuating.
Thus in this respect Palm print Pattern recognition has been introduced which is highly
complex, hopefully insuperable challenge to those who wish to defeat them because
blood vessels are hidden inside the body and furthermore in this there is no physical
contact between the client and system.
In the present research work efforts are made to propose a system which can improve the
performance in terms of accuracy of the palm print patterns and their features, which is
recognized from the given images, a public database of palm prints “SDUMLA-HMT”.
The proposed work includes the Image Processing using MATLAB R2014a and several
other algorithms.
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Al-Waisy, Alaa S., Rami S. R. Qahwaji, Stanley S. Ipson, and Shumoos Al-Fahdawi. "A Robust Face Recognition System Based on Curvelet and Fractal Dimension Transforms." 2015. http://hdl.handle.net/10454/16600.
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yesn this paper, a powerful face recognition system for authentication and identification tasks is presented and a new facial feature extraction approach is proposed. A novel feature extraction method based on combining the characteristics of the Curvelet transform and Fractal dimension transform is proposed. The proposed system consists of four stages. Firstly, a simple preprocessing algorithm based on a sigmoid function is applied to standardize the intensity dynamic range in the input image. Secondly, a face detection stage based on the Viola-Jones algorithm is used for detecting the face region in the input image. After that, the feature extraction stage using a combination of the Digital Curvelet via wrapping transform and a Fractal Dimension transform is implemented. Finally, the K-Nearest Neighbor (K-NN) and Correlation Coefficient (CC) Classifiers are used in the recognition task. Lastly, the performance of the proposed approach has been tested by carrying out a number of experiments on three well-known datasets with high diversity in the facial expressions: SDUMLA-HMT, Faces96 and UMIST datasets. All the experiments conducted indicate the robustness and the effectiveness of the proposed approach for both authentication and identification tasks compared to other established approaches.
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Al-Waisy, Alaa S., Rami S. R. Qahwaji, Stanley S. Ipson, and Shumoos Al-Fahdawi. "A multimodal deep learning framework using local feature representations for face recognition." 2017. http://hdl.handle.net/10454/13122.
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YesThe most recent face recognition systems are mainly dependent on feature representations obtained using either local handcrafted-descriptors, such as local binary patterns (LBP), or use a deep learning approach, such as deep belief network (DBN). However, the former usually suffers from the wide variations in face images, while the latter usually discards the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the DBN is proposed to address the face recognition problem in unconstrained conditions. Firstly, a novel multimodal local feature extraction approach based on merging the advantages of the Curvelet transform with Fractal dimension is proposed and termed the Curvelet–Fractal approach. The main motivation of this approach is that theCurvelet transform, a newanisotropic and multidirectional transform, can efficiently represent themain structure of the face (e.g., edges and curves), while the Fractal dimension is one of the most powerful texture descriptors for face images. Secondly, a novel framework is proposed, termed the multimodal deep face recognition (MDFR)framework, to add feature representations by training aDBNon top of the local feature representations instead of the pixel intensity representations. We demonstrate that representations acquired by the proposed MDFR framework are complementary to those acquired by the Curvelet–Fractal approach. Finally, the performance of the proposed approaches has been evaluated by conducting a number of extensive experiments on four large-scale face datasets: the SDUMLA-HMT, FERET, CAS-PEAL-R1, and LFW databases. The results obtained from the proposed approaches outperform other state-of-the-art of approaches (e.g., LBP, DBN, WPCA) by achieving new state-of-the-art results on all the employed datasets.
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Al-Waisy, Alaa S., Rami S. R. Qahwaji, Stanley S. Ipson, and Shumoos Al-Fahdawi. "A Fast and Accurate Iris Localization Technique for Healthcare Security System." 2015. http://hdl.handle.net/10454/16599.
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yesIn the health care systems, a high security level is required to protect extremely sensitive patient records. The goal is to provide a secure access to the right records at the right time with high patient privacy. As the most accurate biometric system, the iris recognition can play a significant role in healthcare applications for accurate patient identification. In this paper, the corner stone towards building a fast and robust iris recognition system for healthcare applications is addressed, which is known as iris localization. Iris localization is an essential step for efficient iris recognition systems. The presence of extraneous features such as eyelashes, eyelids, pupil and reflection spots make the correct iris localization challenging. In this paper, an efficient and automatic method is presented for the inner and outer iris boundary localization. The inner pupil boundary is detected after eliminating specular reflections using a combination of thresholding and morphological operations. Then, the outer iris boundary is detected using the modified Circular Hough transform. An efficient preprocessing procedure is proposed to enhance the iris boundary by applying 2D Gaussian filter and Histogram equalization processes. In addition, the pupil’s parameters (e.g. radius and center coordinates) are employed to reduce the search time of the Hough transform by discarding the unnecessary edge points within the iris region. Finally, a robust and fast eyelids detection algorithm is developed which employs an anisotropic diffusion filter with Radon transform to fit the upper and lower eyelids boundaries. The performance of the proposed method is tested on two databases: CASIA Version 1.0 and SDUMLA-HMT iris database. The Experimental results demonstrate the efficiency of the proposed method. Moreover, a comparative study with other established methods is also carried out.
Book chapters on the topic "SDUMLA-HMT"
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Yin, Yilong, Lili Liu, and Xiwei Sun. "SDUMLA-HMT: A Multimodal Biometric Database." In Biometric Recognition, 260–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25449-9_33.
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