پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 610 |
| تعداد مقالات | 9,028 |
| تعداد مشاهده مقاله | 67,082,875 |
| تعداد دریافت فایل اصل مقاله | 7,656,353 |
Hand vein recognition with rotation feature matching based on fuzzy algorithm | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، Special Issue، اسفند 2021، صفحه 951-958 اصل مقاله (1.36 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5536 | ||
| نویسندگان | ||
| Haitham S Hasan* 1؛ Mais A Al-Sharqi2 | ||
| 1Business Information Technology Department, Business Informatics College, University of Information Technology and Communications, Baghdad, Iraq. | ||
| 2Bioinformatics Department, BioMedical Informatics College, University of Information Technology and Communications, Baghdad, Iraq. | ||
| تاریخ دریافت: 16 خرداد 1400، تاریخ بازنگری: 07 مرداد 1400، تاریخ پذیرش: 30 مرداد 1400 | ||
| چکیده | ||
| The Bodily motion or emotion, which can be obtained for example from a hand or a face, originates gestures. Every individual has a unique pattern of dorsal hand veins. The vein pattern's orientation changes when one rotates their hand in a particular direction. This study focused on hand-gesture recognition using dorsal hand veins. The aim of this work is a novel technique to track and recognizing hand vein rotation using fuzzy neural network, and the change in orientation was considered as a gesture and measured. The algorithms were tested over various rotations ranging from $-45^{\circ}$ to $+45^{\circ}$. We successfully detected various rotations in both clockwise and anti-clockwise directions, achieving $93\%$ accuracy and a reasonable time execution. This problem can be solved because a person can steer a car wheel merely by rotating his/her hand. An infrared camera captured the rotation of hand veins, so car wheel steering was unnecessary. | ||
| کلیدواژه‌ها | ||
| Complex Walsh transform؛ Dorsal hand vein pattern؛ Feature extraction؛ Fuzzy neural network؛ Sectorization | ||
| مراجع | ||
|
[1] N. AdnanIbraheem and R. Zaman Khan, Survey on various gesture recognition technologies and techniques, Int. J. Comput. Appl. 50(7) (2012) 38–44. [2] H. Al Azawee, S. Husien and M.A.M. Yunus, Encryption function on artificial neural network, Neural Comput. Appl. 27(8) (2016) 2601–2604. [3] M.A. Al-Sharqi and H.S. Hasan, Fuzzy control algorithm for estimation and interaction of dynamic arm motion, Recent Adv. Comput. Sci. Commun. 13(1) (2019) 99–104. [4] P. Arora, G. Chaudhary and S. Srivastava, Exploiting oriented gradient histogram for dorsal vein recognition, 2019 12th Int. Conf. Contemp. Comput. IC3 2019, (2019) 1–4. [5] H. Badi, Recent methods in vision-based hand gesture recognition, Int. J. Data Sci. Anal. 1(2) (2016) 77–87. [6] A.S. Bhosale and M.R. Jadhav, Dorsal hand vein pattern recognition system based on neural network, Proc. Int. Conf. Electron. Commun. Aerosp. Tech. 2017 (2017) 52–55. [7] V. Dixit and A. Agrawal, Real time hand detection & tracking for dynamic gesture recognition, Int. J. Intell. Syst. Appl. 7(8) (2015) 38–44. [8] R.M. Gurav, Vision based hand gesture recognition with Haar classifier and AdaBoost algorithm, Int. J. Lat. Trend. Engin.eering and Tech. 5(2) (2015) 155–160. [9] C. Kauba and A. Uhl, Shedding light on the veins-reflected light or transillumination in hand-vein recognition, Proc. -2018 Int. Conf. Biometrics, ICB 2018 (2018) 283–290. [10] A. Lics´ar and T. Szir´anyi, Hand gesture recognition in camera-projector system, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 3058 (2004) 83–93. [11] K.C. Lim, S.H. Sin, C.W. Lee, W.K. Chin, J. Lin, K. Nguyen, Q.H. Nguyen, B.P. Nguyen and M. Chua, Videobased skeletal feature extraction for hand gesture recognition, ACM Int. Conf. Proceeding Ser. (2020) 108–112. [12] G.R.S. Murthy and R.S. Jadon, Hand gesture recognition using neural networks, 2010 IEEE 2nd Int. Adv. Comput. Conf. IACC 2010, no. March 2015, (2010) 134–138. [13] H. Qin, M.A. El Yacoubi, J. Lin and B. Liu, An iterative deep neural network for hand-vein verification, IEEE Access 7 (2019) 34823–34837. [14] T. Saini and S. Sivani, Real time vision hand gesture recognition based media control via LAN & Wireless hardware control, Int. J. Multidiscip. Educ. Res. 3 (2013) 3129–3133. [15] R.P. Sharma and G.K. Verma, Human computer interaction using hand gesture, Procedia Comput. Sci. 54 (2015) 721–727. [16] S.M. Shitole, S.B. Patil and S.P. Narote, Dynamic hand gesture recognition using PCA, Pruning and ANN, Int. J. Comput. Appl. 74(2) (2013) 24–29. [17] K. Vasagiri and S.R. Parvata, Dorsal hand vein biometric authentication using complex Walsh transform, Proc. 2016 2nd Int. Conf. Appl. Theor. Comput. Commun. Technol. iCATccT 2016, (2017) 533–537. [18] W. Wu, S.J. Elliott, S. Lin, S. Sun and Y. Tang, Review of palm vein recognition, IET Biomet. 9(1) (2020) 1–10.[19] J. Wu, L. Wang, G. Yang, L. Senhadji, L. Luo and H. Shu, Sliding conjugate symmetric sequency-ordered complex hadamard transform: Fast algorithm and applications, IEEE Trans. Circuits Syst. I Regul. Pap. 59(6) (2012) 1321–1334. [20] L. Yang, J. Chen and W. Zhu, Dynamic hand gesture recognition based on a leap, Sensors 20(7) (2020) 1–17. [21] H. Zhou and T.S. Huang, Tracking articulated hand motion with eigen dynamics analysis, Proc. IEEE Int. Conf. Comput. Vis. 2(Iccv) (2003) 1102–1109. | ||
|
آمار تعداد مشاهده مقاله: 43,779 تعداد دریافت فایل اصل مقاله: 599 |
||