A face recognition application for Alzheimer’s patients using ESP32-CAM and Raspberry Pi
Tóm tắt
Từ khóa
Tài liệu tham khảo
Lin, Z.H., Li, Y. Z.: Design and Implementation of Classroom Attendance System Based on Video Face Recognition. In: Proc. - 2019 Int. Conf. Intell. Transp. Big Data Smart City, ICITBS 2019, pp 385–388, (2019), doi: https://doi.org/10.1109/ICITBS.2019.00101
Chuo, Y. H., Sheu, R. K., Chen, L. C.: Design and implementation of a cross-camera suspect tracking system. In: 2019 Int. Autom. Control Conf. CACS 2019, pp. 1–6, (2019)https://doi.org/10.1109/CACS47674.2019.9024367
Teixeira, E.H., Mafra, S.B., Rodrigues, J.J.P.C., Da Silveira, W.A.A.N., Diallo, O.: A review and construction of a real-time facial recognition system. Inst. Nac. Telecomun. (2020). https://doi.org/10.5753/sbcup.2020.11225
Almabdy, S., Elrefaei, L.: Deep convolutional neural network-based approaches for face recognition. Appl. Sci. (2019). https://doi.org/10.3390/app9204397
Boufenar, C., Kerboua, A., Batouche, M.: Investigation on deep learning for off-line handwritten Arabic character recognition. Cogn. Syst. Res. 50, 180–195 (2018). https://doi.org/10.1016/j.cogsys.2017.11.002
Ho, H.T., Chellappa, R.: Pose-invariant face recognition using Markov random fields. IEEE Trans. Image Process. 22(4), 1573–1584 (2013). https://doi.org/10.1109/TIP.2012.2233489
Phillips, P. J. et al.: Overview of the face recognition grand challenge. In: Proc. - 2005 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition, CVPR 2005, vol. I, pp. 947–954, (2005), https://doi.org/10.1109/CVPR.2005.268
Hariri, W., Tabia, H., Farah, N., Benouareth, A., Declercq, D.: 3D face recognition using covariance based descriptors. Pattern Recognit. Lett. 78, 1–7 (2016). https://doi.org/10.1016/j.patrec.2016.03.028
Martikainen, K., Said, K.: A facial recognition application for elderly care: caregiver verification and identification. KTH R. Inst. Technol. Sch. Electr. Eng. Comput. Sci., (2018), http://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1252357&dswid=5861
Bakhshi, Y., Kaur, S., Verma, P.: A study based on various face recognition algorithms. Int. J. Comput. Appl. 129(13), 16–20 (2015). https://doi.org/10.5120/ijca2015907066
Sanchez-Moreno, A.S., Olivares-Mercado, J., Hernandez-Suarez, A., Toscano-Medina, K., Sanchez-Perez, G., Benitez-Garcia, G.: Efficient face recognition system for operating in unconstrained environments. J. Imaging (2021). https://doi.org/10.3390/jimaging7090161
de Sousa-Britto-Neto, L., Maike, V.R.M.L., Koch, F.L., Baranauskas, M.C.C., Rocha, A.D.R., Goldenstein, S.K.: A wearable face recognition system built into a smartwatch and the blind and low vision users. Lect. Notes Bus. Inf. Process. 241(December), 515–528 (2015). https://doi.org/10.1007/978-3-319-29133-8_25
Zhang, Y.: A computational model of quantitatively measuring the Alzheimer’s disease progression in face identification. Electron. Sci. Technol. Appl. 6(1), 29–33 (2019). https://doi.org/10.18686/esta.v6i1.93
Aljojo, N., et al.: Alzheimer assistant: a mobile application using machine learning. Rev. Română Inform. Autom. 30(4), 7–26 (2020). https://doi.org/10.33436/v30i4y202001
Timeless, “Timeless,” (2019) https://kale-clavichord-7blm.squarespace.com/
Salman, H.M., Rasheed, R.T.: Smart door for handicapped people via face recognition and voice command technique. Eng. Technol. J. 39(1B), 222–230 (2021). https://doi.org/10.30684/etj.v39i1b.1719
Wazwaz, A. A., Herbawi, A. O., Teeti, M. J., Hmeed, S. Y.: Raspberry Pi and computers-based face detection and recognition system. In: 2018 4th Int. Conf. Comput. Technol. Appl. ICCTA 2018, pp. 171–174, (2018)https://doi.org/10.1109/CATA.2018.8398677
Raju, K., Srinivasa-Rao, Y.: Real time implementation of face recognition system on Raspberry Pi. Int. J. Eng. Technol. 7(2), 85–89 (2018). https://doi.org/10.14419/ijet.v7i2.17.11564
Lee, S. J., Jung, S. B., Kwon, J. W., Hong, S. H.: Face detection and recognition using PCA. In: IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, vol. 1, no. December, pp. 84–87, (1999) https://doi.org/10.1109/TENCON.1999.818355
Umm-E-Laila, Khan, M. A., Shaikh, M. K., Bin Mazhar, S. A., Mehboob, K.: Comparative analysis for a real time face recognition system using raspberry Pi. In: 2017 IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2017, vol. 2017-Novem, no. November, pp. 1–4. (2018) https://doi.org/10.1109/ICSIMA.2017.8311984
Wankhede, V., Mule, G., Londhe, R., Tarale, A., Gome, S.: Criminal face recognition using Raspberry Pi. Int. Res. J. Innov. Eng. Technol. 3(12), 1–3 (2019)
Kaur, A., Jadli, A., Sadhu, A., Goyal, S., Mehra, A., Rahul.: Cloud based surveillance using ESP32 CAM. In: Int. Conf. Intell. Technol. Syst. Serv. Internet Everything, ITSS-IoE 2021, no. April 2022, (2021) https://doi.org/10.1109/ITSS-IoE53029.2021.9615334
Dani, P., Adi, P., Wahyu, Y.: Performance evaluation of ESP32 Camera face recognition for various projects. ASCEE, Indones. 02(June), 1 (2021). https://doi.org/10.31763/iota.v2i1.512
Allafi, I., Iqbal, T.: Design and implementation of a low cost web server using ESP32 for real-time photovoltaic system monitoring. In: 2017 IEEE Electr. Power Energy Conf. EPEC 2017, vol. 2017-Octob, no. May 2022, pp. 1–5, (2018) https://doi.org/10.1109/EPEC.2017.8286184
Amato, G., Carrara, F., Falchi, F., Gennaro, C., Vairo, C.: Facial-based intrusion detection system with deep learning in embedded devices. In: ACM Int. Conf. Proceeding Ser., pp. 64–68, (2018) https://doi.org/10.1145/3290589.3290598
Januzaj, Y., Luma, A., Januzaj, Y., Ramaj, V.: Real time access control based on face recognition. November, (2015) https://doi.org/10.15242/iae.iae0615004
Syafeeza, A.R., Mohd-Fitri-Alif, M.K., Nursyifaa-Athirah, Y., Jaafar, A.S., Norihan, A.H., Saleha, M.S.: IoT based facial recognition door access control home security system using raspberry pi. Int. J. Power Electron. Drive Syst. 11(1), 417–424 (2020). https://doi.org/10.11591/ijpeds.v11.i1.pp417-424
Chao, W.-L.: Face recognition. GICE, National Taiwan University, Available online: https://www.orcam.com/en/myeye2/ accessed on 21 May 2022
Yang, S. Luo, P., Loy, C. C., Tang, T.: WIDER FACE: a face detection benchmark. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-Decem, pp. 5525–5533, (2016) https://doi.org/10.1109/CVPR.2016.596
Scherhag, U., Rathgeb, C., Merkle, J., Busch, C.: Deep face representations for differential morphing attack detection. IEEE Trans. Inf. Forensics Secur. 15, 3625–3639 (2020). https://doi.org/10.1109/TIFS.2020.2994750
Khan, K., Khan, R.U., Ali, J., Uddin, I., Khan, S., Roh, B.H.: Race classification using deep learning. Comput. Mater. Contin. 68(3), 3483–3498 (2021). https://doi.org/10.32604/cmc.2021.016535
N. I. o. S. a. T. NIST.: Face recognition technology (FERET). https://www.nist.gov/programs-projects/face-recognition-technology-feret. (2021)
Dwivedi, H., Android Instructor.: Comparing MobileNet models in TensorFlow. (2019) https://www.kdnuggets.com/2019/03/comparing-mobilenet-models-tensorflow.html accessed on 8 Mar 2022
Isuyama, V. K., Albertini, B. D. C.: Comparison of convolutional neural network models for mobile devices. Esc. Politecnica Univ. Sao Paulo (USP), Brazil, pp. 73–83, (2021) https://doi.org/10.5753/wperformance.2021.15724.
Wang, W., Li, Y., Zou, T., Wang, X., You, J., Luo, Y.: A novel image classification approach via dense-Mobilenet models. Mob. Inf. Syst. (2020). https://doi.org/10.1155/2020/7602384
Kadhim, T.A., Smaoui Zghal, N., Hariri, W., Ben Aissa, D.: Face recognition in multiple variations using deep learning and convolutional neural networks. In: 9th Int. Conf. Sci. Electron. Technol. Inf. Telecommun. (SETIT’22), 2022., no. 1, (2022)
Khan, S., Rahmani, H., Shah, S.A.A., Bennamoun, M.: A guide to convolutional neural networks for computer vision. Synth. Lect. Comput. Vis. 8(1), 1–207 (2018). https://doi.org/10.2200/s00822ed1v01y201712cov015
Liu, Y., Zhai, G., Zhao, D., Liu, X.: Frame rate and perceptual quality for HD video. In: Springer Int. Publ. Switz., vol. 9315, (2015) https://doi.org/10.1007/978-3-319-24078-7
Kiran, T.T.J.: Computer vision accuracy analysis with deep learning model using TensorFlow. Int. J. Innov. Res. Comput. Sci. Technol. 8(4), 319–325 (2020). https://doi.org/10.2139/ssrn.3673214
Maruseac, M.: Support for 32 bits architecture. (2019) https://github.com/tensorflow/tensorflow/issues/32315
Adi, P. D. P., Kitagawa, A., Akita, J.: Finger robotic control use M5Stack board and MQTT protocol based. In: 7th Int. Conf. Inf. Technol. Comput. Electr. Eng. ICITACEE 2020—Proc., no. October, pp. 1–6, (2020) https://doi.org/10.1109/ICITACEE50144.2020.9239170
Ahmed, H. M., Rasheed, R. T.: A Raspberry Pi real-time identification system on face recognition. In: Proc. 2020 1st Inf. Technol. to Enhanc. E-Learning other Appl. Conf. IT-ELA 2020, pp. 89–93, (2020) https://doi.org/10.1109/IT-ELA50150.2020.9253107
Nikisins, O., Fuksis, R., Kadikis, A., Greitans, M.: Face recognition system on raspberry Pi. In: 2015 5th Int. Work. Comput. Sci. Eng. Inf. Process. Control Eng. WCSE 2015-IPCE, no. April, (2015) https://doi.org/10.18178/wcse.2015.04.054
Suchitra, Suja, P., Tripathi, S.: Real-time emotion recognition from facial images using Raspberry Pi II. In: 3rd Int. Conf. Signal Process. Integr. Networks, SPIN 2016, pp. 666–670, (2016) https://doi.org/10.1109/SPIN.2016.7566780
Lu, J., Fu, X., Zhang, T.: A smart system for face detection with spatial correlation improvement in IoT environment. In: 2017 IEEE SmartWorld Ubiquitous Intell. Comput. Adv. Trust. Comput. Scalable Comput. Commun. Cloud Big Data Comput. Internet People Smart City Innov. SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017, pp. 1–4, (2018) https://doi.org/10.1109/UIC-ATC.2017.8397550
Gsponer, D.: IoT: building a Raspberry Pi security system with facial recognition. Haaga-Helia (2018)
Kak, S. F., Mustafa, F. M.: Smart home management system based on face recognition index in real-time. In: 2019 Int. Conf. Adv. Sci. Eng. ICOASE 2019, pp. 40–45, (2019) https://doi.org/10.1109/ICOASE.2019.8723673
Munir, A., Kashif Ehsan, S., Mohsin Raza, S. M., Mudassir, M.: Face and speech recognition based smart home. In: 2019 Int. Conf. Eng. Emerg. Technol. ICEET 2019, pp. 1–5, (2019) https://doi.org/10.1109/CEET1.2019.8711849
Saputra, R., Surantha, N.: Smart and real-time door lock system for an elderly user based on face recognition. Bull. Electr. Eng. Inform. 10(3), 1345–1355 (2021). https://doi.org/10.11591/eei.v10i3.2955
Orna, G., Benitez, D. S., Perez, N.: A low-cost embedded facial recognition system for door access control using deep learning. In: 2020 IEEE Andescon, Andescon 2020, pp. 0–5, (2020) https://doi.org/10.1109/ANDESCON50619.2020.9271984
Gunawan, T.S., Gani, M.H.H., Rahman, F.D.A., Kartiwi, M.: Development of face recognition on raspberry pi for security enhancement of smart home system. Indones. J. Electr. Eng. Inform. 5(4), 317–325 (2017). https://doi.org/10.11591/ijeei.v5i4.361
Vamsi, T.K., Sai, K.C., Vijayalakshmi, M.: Face recognition based door unlocking system using Raspberry Pi Thulluri. Int. J. Adv. Res. Ideas Innov. Technol. 5(2), 1320–1324 (2019)
Hasban, A. S. et al.: Face recognition for Student Attendance using Raspberry Pi. In: APACE 2019 - 2019 IEEE Asia–Pacific Conf. Appl. Electromagn. Proc., no. November, pp. 1–5, (2019) https://doi.org/10.1109/APACE47377.2019.9020758
Nagpal, G. S., Singh, G., Singh, J., Yadav, N.: Facial detection and recognition using OpenCV on Raspberry Pi Zero. In: Proc. - IEEE 2018 Int. Conf. Adv. Comput. Commun. Control Networking, ICACCCN 2018, pp. 945–950, (2018) https://doi.org/10.1109/ICACCCN.2018.8748389
Singh, S., Ramya, R., Sushma, V., Roshini, S., Pavithra, R.: Facial recognition using machine learning algorithms on Raspberry Pi. In: 4th Int. Conf. Electr. Electron. Commun. Comput. Technol. Optim. Tech. ICEECCOT 2019, pp. 197–202, (2019) https://doi.org/10.1109/ICEECCOT46775.2019.9114716
Nadafa, R.A., Hatturea, S.M., Bonala, V.M., Naikb, S.P.: Home security against human intrusion using Raspberry Pi. Procedia Comput. Sci. 167, 1811–1820 (2020). https://doi.org/10.1016/j.procs.2020.03.200
Rok Novosel, B. M., Ziga Emersic, P. P., Struc V.: Face recognition with Raspberry Pi for IoT environments. In: ERK Portorož, no. September, pp. 477–480, (2017) https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/novoselface_recognition.pdf
Parthornratt, T., Burapanonte, N., Gunjarueg, W.: People identification and counting system using Raspberry pi. In: 2016 Int. Conf. Electron. Information, Commun., pp. 1–5, (2016)
Preetha, J., Manirathnam, M., Chaitanya, A., Raj, R.P.: Raspberry Pi based face recognition system. Int. J. Eng. Res. Technol. 8(08), 1–4 (2020)