<?xml version="1.0" encoding="UTF-8"?>
<doi_batch version="4.3.0" xmlns="http://www.crossref.org/doi_resources_schema/4.3.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/doi_resources_schema/4.3.0 http://www.crossref.org/schema/deposit/doi_resources4.3.0.xsd">
<head>
<doi_batch_id>a3b27de2-9e04-4ab9-b916-70bbca914fd0</doi_batch_id>
<depositor>
<name>beie</name>
<email_address>director@blueeyesintelligence.org</email_address>
</depositor>
</head>
<body>
<doi_citations>
<doi>10.35940/ijitee.D8430.0210421</doi>
<citation_list><citation key="ref0"><doi>10.5120/1424-1659</doi><unstructured_citation>Adeoye, Olufemi Sunday. &quot;A survey of emerging biometric technologies.&quot; International Journal of Computer Applications 9.10 (2010): 1-5.‏</unstructured_citation></citation><citation key="ref1"><doi>10.1109/TCSVT.2003.818349</doi><unstructured_citation>An introduction to biometric recognition IEEE Trans. Circ. Syst., Vid. Technol., 14 (1) (2004), pp. 4-20</unstructured_citation></citation><citation key="ref2"><doi>10.1016/j.aej.2015.10.003</doi><unstructured_citation>Usha, K., and M. Ezhilarasan. &quot;Fusion of geometric and texture features for finger knuckle surface recognition.&quot; Alexandria Engineering Journal 55.1 (2016): 683-697.‏</unstructured_citation></citation><citation key="ref3"><doi>10.1016/j.microc.2007.11.008</doi><unstructured_citation>Marini, Federico, et al. &quot;Artificial neural networks in chemometrics: History, examples and perspectives.&quot; Microchemical journal 88.2 (2008): 178-185.‏</unstructured_citation></citation><citation key="ref4"><doi>10.1109/CVPR.2016.265</doi><unstructured_citation>Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. &quot;Image style transfer using convolutional neural networks.&quot; Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.‏</unstructured_citation></citation><citation key="ref5"><doi>10.1109/TMI.2016.2548501</doi><unstructured_citation>Moeskops, Pim, et al. &quot;Automatic segmentation of MR brain images with a convolutional neural network.&quot; IEEE transactions on medical imaging 35.5 (2016): 1252-1261.‏</unstructured_citation></citation><citation key="ref6"><doi>10.1109/CVPR.2016.265</doi><unstructured_citation>Gatys, Leon, Alexander S. Ecker, and Matthias Bethge. &quot;Texture synthesis using convolutional neural networks.&quot; Advances in neural information processing systems. 2015.‏</unstructured_citation></citation><citation key="ref7"><unstructured_citation>Wang, Tao, et al. &quot;End-to-end text recognition with convolutional neural networks.&quot; Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012). IEEE, 2012.‏</unstructured_citation></citation><citation key="ref8"><doi>10.1109/ICARCV.2014.7064414</doi><unstructured_citation>Q. Li, W. Cai, X. Wang, Y. Zhou, D. D. Feng and M. Chen, &quot;Medical image classification with convolutional neural network,&quot; 2014 13th International Conference on Control Automation Robotics &amp; Vision (ICARCV), Singapore, 2014, pp. 844-848.</unstructured_citation></citation><citation key="ref9"><doi>10.1109/ACCESS.2018.2886573</doi><unstructured_citation>Hammad, M., Liu, Y., &amp; Wang, K. (2019). Multimodal Biometric Authentication Systems Using Convolution Neural Network Based on Different Level Fusion.</unstructured_citation></citation><citation key="ref10"><doi>10.1109/CVPR.2012.6248110</doi><unstructured_citation>Cireşan, Dan, Ueli Meier, and Jürgen Schmidhuber. &quot;Multi-column deep neural networks for image classification.&quot; arXiv preprint arXiv:1202.2745 (2012).‏</unstructured_citation></citation><citation key="ref11"><doi>10.1109/TIP.2017.2765830</doi><unstructured_citation>X. Yin and X. Liu, &quot;Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition,&quot; in IEEE Transactions on Image Processing, vol. 27, no. 2, pp. 964-975, Feb. 2018.</unstructured_citation></citation><citation key="ref12"><doi>10.1109/FG.2017.82</doi><unstructured_citation>H. Jiang and E. Learned-Miller, &quot;Face Detection with the Faster R-CNN,&quot; 2017 12th IEEE International Conference on Automatic Face &amp; Gesture Recognition (FG 2017), Washington, DC, 2017, pp. 650-657.</unstructured_citation></citation><citation key="ref13"><doi>10.1109/CVPR.2015.7299170</doi><unstructured_citation>Li, Haoxiang, et al. &quot;A convolutional neural network cascade for face detection.&quot; Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.‏</unstructured_citation></citation><citation key="ref14"><doi>10.1016/j.patcog.2006.10.011</doi><unstructured_citation>Lauer, Fabien, Ching Y. Suen, and Gérard Bloch. &quot;A trainable feature extractor for handwritten digit recognition.&quot; Pattern Recognition 40.6 (2007): 1816-1824.‏</unstructured_citation></citation><citation key="ref15"><doi>10.1109/CVPR.2016.239</doi><unstructured_citation>Krafka, Kyle, et al. &quot;Eye tracking for everyone.&quot; Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.‏</unstructured_citation></citation><citation key="ref16"><doi>10.1109/CNNA.2006.341650</doi><unstructured_citation>Malki, Suleyman, Yu Fuqiang, and Lambert Spaanenburg. &quot;Vein feature extraction using DT-CNNs.&quot; 2006 10th International Workshop on Cellular Neural Networks and Their Applications. IEEE, 2006.‏</unstructured_citation></citation><citation key="ref17"><doi>10.1016/j.ijleo.2017.09.064</doi><unstructured_citation>Wang, Jun, and Guoqing Wang. &quot;Hand-dorsa vein recognition with structure growing guided CNN.&quot; Optik 149 (2017): 469-477.‏</unstructured_citation></citation><citation key="ref18"><doi>10.1109/BIBM.2017.8217830</doi><unstructured_citation>Wan, Haipeng, et al. &quot;Dorsal hand vein recognition based on convolutional neural networks.&quot; 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017.‏</unstructured_citation></citation><citation key="ref19"><doi>10.1109/ICNNSP.2008.4590300</doi><unstructured_citation>Ho-Joon Kim, J. S. Lee and J. Park, &quot;Dynamic hand gesture recognition using a CNN model with 3D receptive fields,&quot; 2008 International Conference on Neural Networks and Signal Processing, Nanjing, 2008, pp. 14-19.</unstructured_citation></citation><citation key="ref20"><doi>10.1109/CoASE.2014.6899454</doi><unstructured_citation>H. Lin, M. Hsu and W. Chen, &quot;Human hand gesture recognition using a convolution neural network,&quot; 2014 IEEE International Conference on Automation Science and Engineering (CASE), Taipei, 2014, pp. 1038-1043.</unstructured_citation></citation><citation key="ref21"><journal_title>Cluster Computing</journal_title><author>Li</author><cYear>2019</cYear><doi>10.1007/s10586-017-1435-x</doi><article_title>Hand gesture recognition based on convolution neural network</article_title><unstructured_citation>Li, Gongfa &amp; Tang, Heng &amp; Sun, Ying &amp; Kong, Jianyi &amp; Jiang, Guozhang &amp; Jiang, Du &amp; Tao, Bo &amp; Xu, Shuang &amp; Liu, Honghai. (2019). Hand gesture recognition based on convolution neural network. Cluster Computing. 22. 10.1007/s10586-017-1435-x.</unstructured_citation></citation><citation key="ref22"><journal_title>266-275</journal_title><author>Alpar</author><cYear>2017</cYear><doi>10.1007/978-3-319-54430-4_26</doi><article_title>A New Feature Extraction in Dorsal Hand Recognition by Chromatic Imaging</article_title><unstructured_citation>Alpar, Orcan &amp; Krejcar, Ondrej. (2017). A New Feature Extraction in Dorsal Hand Recognition by Chromatic Imaging. 266-275. 10.1007/978-3-319-54430-4_26.</unstructured_citation></citation><citation key="ref23"><doi>10.1109/TIP.2006.873439</doi><unstructured_citation>E. Yoruk, E. Konukoglu, B. Sankur and J. Darbon, &quot;Shape-based hand recognition,&quot; in IEEE Transactions on Image Processing, vol. 15, no. 7, pp. 1803-1815, July 2006.</unstructured_citation></citation><citation key="ref24"><unstructured_citation>Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... &amp; Kudlur, M. (2016). Tensorflow: A system for large-scale machine learning. In 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16) (pp. 265-283).</unstructured_citation></citation><citation key="ref25"><unstructured_citation>Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., ... &amp; Ghemawat, S. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.</unstructured_citation></citation><citation key="ref26"><doi>10.1109/UBMK.2017.8093521</doi><unstructured_citation>Ertam, F., &amp; Aydın, G. (2017, October). Data classification with deep learning using Tensorflow. In 2017 International Conference on Computer Science and Engineering (UBMK) (pp. 755-758). IEEE.</unstructured_citation></citation><citation key="ref27"><doi>10.1109/DLS49591.2019.00011</doi><unstructured_citation>Mattmann, C. A., &amp; Zhang, Z. (2019, November). Deep Facial Recognition using Tensorflow. In 2019 IEEE/ACM Third Workshop on Deep Learning on Supercomputers (DLS) (pp. 45-51). IEEE.</unstructured_citation></citation><citation key="ref28"><doi>10.1007/978-3-319-72727-1_6</doi><unstructured_citation>[29] Soriano, D., Aguilar, C., Ramirez-Morales, I., Tusa, E., Rivas, W., &amp; Pinta, M. (2017, November). Mammogram classification schemes by using convolutional neural networks. In International Conference on Technology Trends (pp. 71-85). Springer, Cham.</unstructured_citation></citation><citation key="ref29"><journal_title>Advances in Space Research</journal_title><author>Perez</author><volume>63</volume><issue>5</issue><first_page>1607</first_page><cYear>2019</cYear><doi>10.1016/j.asr.2018.11.011</doi><article_title>Using TensorFlow-based Neural Network to estimate GNSS single frequency ionospheric delay (IONONet)</article_title><unstructured_citation>Perez, R. O. (2019). Using TensorFlow-based Neural Network to estimate GNSS single frequency ionospheric delay (IONONet). Advances in Space Research, 63(5), 1607-1618.</unstructured_citation></citation><citation key="ref30"><doi>10.1109/UBMK.2017.8093521</doi><unstructured_citation>F. Ertam and G. Aydın, &quot;Data classification with deep learning using Tensorflow,&quot; 2017 International Conference on Computer Science and Engineering (UBMK), Antalya, 2017, pp. 755-758.</unstructured_citation></citation><citation key="ref31"><unstructured_citation>Bhandare, Ashwin, et al. &quot;Applications of convolutional neural networks.&quot; International Journal of Computer Science and Information Technologies 7.5 (2016): 2206-2215.‏</unstructured_citation></citation><citation key="ref32"><doi>10.1016/j.patcog.2010.04.006</doi><unstructured_citation>Yu, Jinhua, Jinglu Tan, and Yuanyuan Wang. &quot;Ultrasound speckle reduction by a SUSAN-controlled anisotropic diffusion method.&quot; Pattern recognition 43.9 (2010): 3083-3092.‏</unstructured_citation></citation><citation key="ref33"><doi>10.1109/83.902291</doi><unstructured_citation>Chan, Tony F., and Luminita A. Vese. &quot;Active contours without edges.&quot; IEEE Transactions on image processing 10.2 (2001): 266-277.</unstructured_citation></citation><citation key="ref34"><unstructured_citation>Nair, Vinod, and Geoffrey E. Hinton. &quot;Rectified linear units improve restricted boltzmann machines.&quot; Proceedings of the 27th international conference on machine learning (ICML-10). 2010.‏</unstructured_citation></citation><citation key="ref35"><unstructured_citation>Simonyan, Karen, and Andrew Zisserman. &quot;Very deep convolutional networks for large-scale image recognition.&quot; arXiv preprint arXiv:1409.1556 (2014).</unstructured_citation></citation><citation key="ref36"><unstructured_citation>Afifi, M. 11K Hands: Gender recognition and biometric identification using a large dataset of hand</unstructured_citation></citation><citation key="ref37"><doi>10.1007/s11042-019-7424-8</doi><unstructured_citation>images. Multimed Tools Appl 78, 20835-20854 (2019).</unstructured_citation></citation><citation key="ref38"><unstructured_citation>An end-to-end open source machine learning platform ( https://www.tensorflow.org/).</unstructured_citation></citation><citation key="ref39"><doi>10.1109/SP.2017.54</doi><unstructured_citation>Song, Y., Cai, Z., &amp; Zhang, Z.-L. (2017). Multi-touch Authentication Using Hand Geometry and Behavioral Information. 2017 IEEE Symposium on Security and Privacy (SP). doi:10.1109/sp.2017.54</unstructured_citation></citation><citation key="ref40"><doi>10.1109/SISY50555.2020.9217068</doi><unstructured_citation>Oldal, L. G., &amp; Kovacs, A. (2020). Hand geometry and palm print-based authentication using image processing. 2020 IEEE 18th International Symposium</unstructured_citation></citation><citation key="ref41"><doi>10.1109/TIE.2018.2823686</doi><unstructured_citation>Gupta, P., &amp; Gupta, P. (2018). Multi-biometric Authentication System Using Slap Fingerprints, Palm Dorsal Vein, and Hand Geometry. IEEE Transactions</unstructured_citation></citation><citation key="ref42"><doi>10.1109/ICB2018.2018.00046</doi><unstructured_citation>Lu, D., Huang, D., Deng, Y., &amp; Alshamrani, A. (2018). Multifactor User Authentication with In-Air-Handwriting and Hand Geometry. 2018 International Conference on Biometrics</unstructured_citation></citation></citation_list>
</doi_citations>
</body>
</doi_batch>
