Computer vision based classroom attendance management system-with speech output using LBPH algorithm
Daily attendance marking is a common and important activity in schools and colleges for checking the performance of students. Manual Attendance maintaining is difficult to process, especially for a large group of students. Some automated systems developed to overcome these difficulties, have drawbac...
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| Published in | International journal of speech technology Vol. 23; no. 4; pp. 779 - 787 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.12.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1381-2416 1572-8110 |
| DOI | 10.1007/s10772-020-09739-2 |
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| Abstract | Daily attendance marking is a common and important activity in schools and colleges for checking the performance of students. Manual Attendance maintaining is difficult to process, especially for a large group of students. Some automated systems developed to overcome these difficulties, have drawbacks like cost, fake attendance, accuracy, intrusiveness. To overcome these drawbacks, there is a need for a smart and automated attendance system. Traditional face recognition systems employ methods to identify a face from the given input but the results are not usually accurate and precise as desired. The system described in this we aim to deviate from such traditional systems and introduce a new approach to identify a student using a face recognition system, the generation of a facial Model. This describes the working of the face recognition system that will be deployed as an Automated Attendance System in a classroom environment. The proposed smart classroom system was tested for a classroom with 20 students at K L University Andhra Pradesh, Vijayawada, India and we got the experimental results to demonstrate the train and test accuracy of 97.67% and 96.66%, respectively. In this paper we selecting of the face recognition and detection giving result using Python language in PYCHARM tool. This requires high end specifications of a system in order to get better results. It won’t run on all the small specification systems. So, this can run only a small database and compare them with the face required. |
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| AbstractList | Daily attendance marking is a common and important activity in schools and colleges for checking the performance of students. Manual Attendance maintaining is difficult to process, especially for a large group of students. Some automated systems developed to overcome these difficulties, have drawbacks like cost, fake attendance, accuracy, intrusiveness. To overcome these drawbacks, there is a need for a smart and automated attendance system. Traditional face recognition systems employ methods to identify a face from the given input but the results are not usually accurate and precise as desired. The system described in this we aim to deviate from such traditional systems and introduce a new approach to identify a student using a face recognition system, the generation of a facial Model. This describes the working of the face recognition system that will be deployed as an Automated Attendance System in a classroom environment. The proposed smart classroom system was tested for a classroom with 20 students at K L University Andhra Pradesh, Vijayawada, India and we got the experimental results to demonstrate the train and test accuracy of 97.67% and 96.66%, respectively. In this paper we selecting of the face recognition and detection giving result using Python language in PYCHARM tool. This requires high end specifications of a system in order to get better results. It won’t run on all the small specification systems. So, this can run only a small database and compare them with the face required. |
| Author | Harish, P. Kaushik, N. Lokesh, G. Bhavana, D. Mounisha, E. Kumar, K. Kishore Tej, D. Ravi |
| Author_xml | – sequence: 1 givenname: D. surname: Bhavana fullname: Bhavana, D. email: bhavanaece@kluniversity.in organization: Department of Electronics and Communication Engineering, KLEF (Deemed to be University) Guntur – sequence: 2 givenname: K. Kishore surname: Kumar fullname: Kumar, K. Kishore organization: Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation – sequence: 3 givenname: N. surname: Kaushik fullname: Kaushik, N. organization: Department of Electronics and Communication Engineering, KLEF (Deemed to be University) Guntur – sequence: 4 givenname: G. surname: Lokesh fullname: Lokesh, G. organization: Department of Electronics and Communication Engineering, KLEF (Deemed to be University) Guntur – sequence: 5 givenname: P. surname: Harish fullname: Harish, P. organization: Department of Electronics and Communication Engineering, KLEF (Deemed to be University) Guntur – sequence: 6 givenname: E. surname: Mounisha fullname: Mounisha, E. organization: Department of Electronics and Communication Engineering, KLEF (Deemed to be University) Guntur – sequence: 7 givenname: D. Ravi surname: Tej fullname: Tej, D. Ravi organization: Department of Electronics and Communication Engineering, SRK Institute of Technology |
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| Keywords | Face detection method Bit- byte conversion methods Image processing HAAR features Features matching Query image Expressions Face recognition method Features extraction HOG features |
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| References | NagaJyothi, G., & SriDevi, S. (2017). Distributed arithmetic architectures for fir filters-a comparative review. In: 2017 International conference on wireless communications, signal processing and networking (WiSPNET), pp. 2684–2690. IEEE Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05) (Vol. 1, pp. 886–893). IEEE Jayant, N.K., Borra, S. (2016). Attendance management system using hybrid face recognition techniques. In 2016 Conference on advances in signal processing (CASP), pp. 412–417. IEEE Samet, R., & Tanriverdi, M. (2017) Face recognition-based mobile automatic classroom attendance management system. In: 2017 International conference on cyberworlds (CW), pp. 253–256. IEEE NagaJyothiGSrideviSHigh speed and low area decision feed-back equalizer with novel memory less distributed arithmetic filterMultimedia Tools and Applications20197823326793269310.1007/s11042-018-7038-6 Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818–2826 Grande, N. J., & Sridevi, S. (2017). Asic implementation of shared lut based distributed arithmetic in fir filter. In 2017 International conference on microelectronic devices, Circuits and Systems (ICMDCS), pp. 1–4. IEEE JyothiGNSrideviSLow power, low area adaptive finite impulse response filter based on memory less distributed arithmeticJournal of Computational and Theoretical Nanoscience2018156–72003200810.1166/jctn.2018.7397 King, D. E. (2015). Max-margin object detection. arXiv:1502.00046 Salim, O. A. R., Olanrewaju, R. F., & Balogun, W. A. (2018) Class attendance management system using face recognition. In: 2018 7th International conference on computer and communication engineering (ICCCE), pp. 93–98. IEEE Edelman, S., Reisfeld, D., & Yeshurun, Y. (1994). A system for face recognition that learns from examples. In Proc. European Conf. Computer Vision, pp. 787–791 Khorsheed, J. A., & Yurtkan, K. (2016). Analysis of local binary patterns for face recognition under varying facial expressions. In: 2016 24th signal processing and communication application conference (SIU), pp. 2085–2088. IEEE HallinanPWA low-dimensional representation of human faces for arbitrary lighting conditionsCVPR199494995999 Jyothi, G. N., & Sriadibhatla, S. (2019). Asic implementation of low power, area efficient adaptive fir filter using pipelined da. In Microelectronics, electromagnetics and telecommunications, pp. 385–394. Berlin: Springer Jyothi, G. N., Anusha, G., Kumar, N. D., & Kundu, D. (2019). Design of finfet based dram cell for low power applications. In Computer-aided developments: electronics and communication: proceeding of the first annual conference on computer-aided developments in electronics and communication (CADEC-2019), Vellore Institute of Technology, Amaravati, India, 2–3 March 2019, p. 35. Boca Raton: CRC Press JainVLearned-MillerEFddb: a benchmark for face detection in unconstrained settings2010UMass Amherst technical reportTech. Rep. Shehu, V., & Dika, A. (2010). Using real time computer vision algorithms in automatic attendance management systems. In: Proceedings of the ITI 2010, 32nd international conference on information technology interfaces, pp. 397–402. IEEE NagaJyothiGSrideviSHigh speed low area obc da based decimation filter for hearing aids applicationInternational Journal of Speech Technology20192311112110.1007/s10772-019-09660-3 9739_CR18 9739_CR17 GN Jyothi (9739_CR9) 2018; 15 9739_CR10 9739_CR12 9739_CR11 9739_CR16 9739_CR1 9739_CR15 9739_CR2 9739_CR3 V Jain (9739_CR5) 2010 G NagaJyothi (9739_CR14) 2019; 23 9739_CR6 9739_CR7 G NagaJyothi (9739_CR13) 2019; 78 PW Hallinan (9739_CR4) 1994; 94 9739_CR8 |
| References_xml | – reference: HallinanPWA low-dimensional representation of human faces for arbitrary lighting conditionsCVPR199494995999 – reference: JainVLearned-MillerEFddb: a benchmark for face detection in unconstrained settings2010UMass Amherst technical reportTech. Rep. – reference: Jyothi, G. N., Anusha, G., Kumar, N. D., & Kundu, D. (2019). Design of finfet based dram cell for low power applications. In Computer-aided developments: electronics and communication: proceeding of the first annual conference on computer-aided developments in electronics and communication (CADEC-2019), Vellore Institute of Technology, Amaravati, India, 2–3 March 2019, p. 35. Boca Raton: CRC Press – reference: Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05) (Vol. 1, pp. 886–893). IEEE – reference: Grande, N. J., & Sridevi, S. (2017). Asic implementation of shared lut based distributed arithmetic in fir filter. In 2017 International conference on microelectronic devices, Circuits and Systems (ICMDCS), pp. 1–4. IEEE – reference: Salim, O. A. R., Olanrewaju, R. F., & Balogun, W. A. (2018) Class attendance management system using face recognition. In: 2018 7th International conference on computer and communication engineering (ICCCE), pp. 93–98. IEEE – reference: Shehu, V., & Dika, A. (2010). Using real time computer vision algorithms in automatic attendance management systems. In: Proceedings of the ITI 2010, 32nd international conference on information technology interfaces, pp. 397–402. IEEE – reference: Edelman, S., Reisfeld, D., & Yeshurun, Y. (1994). A system for face recognition that learns from examples. In Proc. European Conf. Computer Vision, pp. 787–791 – reference: Jayant, N.K., Borra, S. (2016). Attendance management system using hybrid face recognition techniques. In 2016 Conference on advances in signal processing (CASP), pp. 412–417. IEEE – reference: JyothiGNSrideviSLow power, low area adaptive finite impulse response filter based on memory less distributed arithmeticJournal of Computational and Theoretical Nanoscience2018156–72003200810.1166/jctn.2018.7397 – reference: NagaJyothiGSrideviSHigh speed low area obc da based decimation filter for hearing aids applicationInternational Journal of Speech Technology20192311112110.1007/s10772-019-09660-3 – reference: Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818–2826 – reference: Jyothi, G. N., & Sriadibhatla, S. (2019). Asic implementation of low power, area efficient adaptive fir filter using pipelined da. In Microelectronics, electromagnetics and telecommunications, pp. 385–394. Berlin: Springer – reference: King, D. E. (2015). Max-margin object detection. arXiv:1502.00046 – reference: Samet, R., & Tanriverdi, M. (2017) Face recognition-based mobile automatic classroom attendance management system. In: 2017 International conference on cyberworlds (CW), pp. 253–256. IEEE – reference: Khorsheed, J. A., & Yurtkan, K. (2016). Analysis of local binary patterns for face recognition under varying facial expressions. In: 2016 24th signal processing and communication application conference (SIU), pp. 2085–2088. IEEE – reference: NagaJyothi, G., & SriDevi, S. (2017). Distributed arithmetic architectures for fir filters-a comparative review. In: 2017 International conference on wireless communications, signal processing and networking (WiSPNET), pp. 2684–2690. IEEE – reference: NagaJyothiGSrideviSHigh speed and low area decision feed-back equalizer with novel memory less distributed arithmetic filterMultimedia Tools and Applications20197823326793269310.1007/s11042-018-7038-6 – ident: 9739_CR3 doi: 10.1109/ICMDCS.2017.8211705 – ident: 9739_CR15 doi: 10.1109/ICCCE.2018.8539274 – volume: 94 start-page: 995 year: 1994 ident: 9739_CR4 publication-title: CVPR – ident: 9739_CR7 doi: 10.1201/9780429340710-5 – volume: 23 start-page: 111 year: 2019 ident: 9739_CR14 publication-title: International Journal of Speech Technology doi: 10.1007/s10772-019-09660-3 – ident: 9739_CR2 – volume: 15 start-page: 2003 issue: 6–7 year: 2018 ident: 9739_CR9 publication-title: Journal of Computational and Theoretical Nanoscience doi: 10.1166/jctn.2018.7397 – ident: 9739_CR10 doi: 10.1109/SIU.2016.7496182 – ident: 9739_CR12 doi: 10.1109/WiSPNET.2017.8300250 – volume-title: Fddb: a benchmark for face detection in unconstrained settings year: 2010 ident: 9739_CR5 – ident: 9739_CR11 – ident: 9739_CR6 doi: 10.1109/CASP.2016.7746206 – ident: 9739_CR8 doi: 10.1007/978-981-13-1906-8_40 – ident: 9739_CR18 doi: 10.1109/CVPR.2016.308 – ident: 9739_CR1 doi: 10.1109/CVPR.2005.177 – ident: 9739_CR16 doi: 10.1109/CW.2017.34 – ident: 9739_CR17 – volume: 78 start-page: 32679 issue: 23 year: 2019 ident: 9739_CR13 publication-title: Multimedia Tools and Applications doi: 10.1007/s11042-018-7038-6 |
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| Title | Computer vision based classroom attendance management system-with speech output using LBPH algorithm |
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