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Статті в журналах з теми "SDUMLA-HMT"
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.
Повний текст джерелаNoroz, 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.
Повний текст джерелаSharif, 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.
Повний текст джерелаLi, 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.
Повний текст джерелаHsia, 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.
Повний текст джерелаAhmed, 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.
Повний текст джерелаMahmoud, 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.
Повний текст джерелаYulianto, 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.
Повний текст джерелаSari, 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.
Повний текст джерелаChannegowda, 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.
Повний текст джерелаДисертації з теми "SDUMLA-HMT"
GUPTA, AMIT. "IMPROVED PALM PRINT RECOGNITION." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15794.
Повний текст джерелаAl-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.
Повний текст джерелаn 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.
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.
Повний текст джерелаThe 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.
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.
Повний текст джерелаIn 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.
Частини книг з теми "SDUMLA-HMT"
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.
Повний текст джерела