Effecting of environmental conditions to accuracy rates of face recognition based on IoT solution
- Meennapa Rukhiran, Department of Social Technology Ragamangala University of Technology Tawan-OK, Chanthaburi, Thailand
- Paniti Netinant, College of Information and Communication Technology, Rangsit University, Bangkok, Thailand, Corresponding author; E-mail: firstname.lastname@example.org
- Tzilla Elrad, Concurrent Programming Research Group, Illinois Institute of Technology, IL, Chicago, 60616, USA
There are an increasing number of Internet of Thing (IoT) solutions that are treated and supported us in communication, data storage, maintenance, monitoring, privacy, and security. Advances in face recognition have provided efficiency and security that can be developed through IoT devices for the cheaper prices than the past. Face recognition enables the development on the camera through Raspberry PI 3 importing a machine learning algorithm in the easy way. However, the use of a camera and a face recognition system on IoT can be particularly found missing detection and recognition problems. The challenges on developing the face recognition system has been encouraging, the accuracy progress of face recognition turn out to be many conditions. In our view, the developing recognition procedure is able to discovery the relationships of the factors related to the correctness of a face recognition system in the different conditions of image resolutions, distances, and illuminations. This article discusses the appropriate conditions for the better condition tasks of image resolutions and distances. Finally, we propose the operational semantic, which can calculate on the different environment conditions and shows results that demonstrate the better accuracy rates of the sufficient preparation for face recognition systems.
Bassi, A., Bauer, M., Fiedler, W., & Kranenburg, R. V. (2013). Enabling things to talk. Berlin, Germany: Springer.
Boltz, M., Jalava, M., & Walsh, J. (2010). New methods and combinatorics for bypassing intrusion prevention technologies. Helsinki, Finland: Stonesoft coporation.
Boom, B. J., Beumer, G. M., Spreeuwers, L. J. & Veldhuis, R. N. J. (2006). The effect of image resolution on the performance of a face recognition system. Proceeding of the 9th International Conference on Control, Automation, Robotics and Vision. December 5-8, 2006. NJ, USA. pp. 409-414. DOI: 10.1109/ICARCV.2006.345480
Bora, P. K., Rahman, F., & Hazarika, M. (2015). A review on face recognition approaches. International Journal of Engineering Research & Technology, 4(5), 524-527.
Breitenbacher, D., Homoliak, I., Aung, Y. L., Tippenhauer, N. O., & Elovici, Y. (2019). HADES-IoT: A practical host-based anomaly detection system for IoT devices. Proceeding of the ACM Conference on Computer and Communications Security. July 9-12, 2019. Auckland, New Zealand. pp. 479-484. DOI: 10.1145/3321705.3329847
Brunelli, R., & Poggio, T. (1993). Face recognition: Features versus Templates. IEEE Transaction on Pattern Analysis and Machine Intelligence, 15(10), 1042-1052.
Calvo, G., Baruque, B., & Corchado, E. (2013). Study of the pre-processing impact in a facial recognition system. Proceeding of the International Conference on Hybrid Artificial Intelligence Systems. September 11-13, 2013. Salamanca, Spain. pp. 334-344. DOI: 10.1007/978-3-642-40846-5_34
Dardas, A. H., & Georganas, N. D. (2011). Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques. IEEE Transactions on Instrumentation and Measurement, 60(11), 3592-3607. DOI: 10.1109/TIM.2011.2161140
Devi, N. S., & Hemachandran, K. (2013). Automatic face recognition system using pattern recognition techniques: A survey. International Journal of Computer Applications, 83(5), 10-13. DOI: 10.5120/14443-2602
Geng, X., Zhou, Z., & Smith-Miles, K. (2008). Individual stable space: An approach to face recognition under uncontrolled conditions. IEEE Transactions on Neural Networks, 19(8), 1354-1368. DOI: 10.1109/TNN.2008.2000275
Kerdvibulvech, C. (2015). Hand tracking by extending distance transform and hand model in real-time. Pattern Recognition and Image Analysis, 25, 437-441. DOI: 10.1134/S1054661815030098
Lemieux, A., & Parizeau, M. (2002). Experiments on eigenfaces robustness. Proceeding of the 16th International Conference on Pattern Recognition. Aug 11-15, 2002. Quebec, Canada. pp. 421-424. DOI: 10.1109/ICPR.2002.1044743
Li, S. Z. (2005). Handbook of face recognition. In Li, S. Z., & Jain, A. K. Chapter 2. Face Detection. New York, USA: Springer-Verlag.
Liao, S., Yi, D., Lei, Z., Qin, R., & Li, S. Z. (2009). Heterogeneous face recognition from local structures of normalized appearance. Proceeding of the Third International Conference on Advances in Biometrics. June 2-5, 2009. Alghero, Italy. pp. 209-218. DOI: 10.1007/978-3-642-01793-3_22
Liu, C. Y. J., & Wilkinson, C. (2020). Image conditions for machine-based face recognition of juvenile faces. Science & Justice, 60(1), 43-52. DOI: 10.1016/j.scijus.2019.10.001
Mano, L. Y., Faical, B. S., Nakamura, L. H. V., Gomes, P. H., Libralon, G. L., Menequete, R. I., .. .. .. Ueyama, J. (2016). Exploiting IoT technologies for enhancing health smart homes through patient identification and emotion recognition. Computer Communications, 89(C), 178-190. DOI: 10.1016/j.comcom.2016.03.010
Marciniak, T., Chmielewska, A., Weychan, R., Parzych, M., & Dabrowski, A. (2015). Influence of low resolution of images on reliability of face detection and recognition. Multimedia Tools and Applications, 74(12), 4329-4349. DOI: 10.1007/s11042-013-1568-8
Martinkauppi, J. B., & Pietikainen, M. (2005). Handbook of face recognition. In Li, S. Z., & Jain, A. K. Chapter 6. Facial Skin Color Modeling. New York, USA: Springer-Verlag.
Mudunuri, S. P., & Biswas, S. (2016). Low resolution face recognition across variations in pose and illumination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(5), 1034-1040. DOI: 10.1109/TPAMI.2015.2469282
Nikan, S., & Ahmadi, M. (2018). A modified technique for face recognition under degraded conditions. Journal of Visual Communication and Image Representation, 55, 742-755. DOI: 10.1016/j.jvcir.2018.08.007
Nikisins, O., Fuksis, R., Kadikis, A., & Greitans, M. (2015). Face recognition system on Raspberry Pi. Proceeding of the International Conference on Information Processing and Control Engineering. April 17-19, 2015. Moscow, Russia. pp. 322-326. DOI: 10.1145/3321705.3329847.
Novosel, R., Meden, B., Emersic, Z., Struc, V., & Peer, P. (2017). Face recognition with Raspberry Pi for IoT environments. Proceeding of the International Electrotechnical and Computer Science Conference. September 25-26, 2017. Portoroz, Slovenia. pp. 477-480.
Pereira, T., Anjos, A., & Marcel, S. (2019). Heterogeneous face recognition using domain specific units. IEEE Transactions on Information Forensics and Security, 14(7), 1803-1816. Doi: 10.1109/TIFS.2018.2885284
Ramachandra, R., & Busch, C. (2017). Presentation attack detection methods for face recognition systems: A comprehensive survey. ACM Computing Surveys, 50(1), 8:1- 8:37. DOI: 10.1145/3038924.
Rukhiran, M., & Netinant, P. (2018). Design of house bookkeeping software components based on separation of concerns. Journal of Current Science and Technology, 8(1), 21-31. DOI: 10.14456/jcst.2018.3
Saleem, J., Hammoudeh, M., Raza, U., Adebisi, B., & Ande, R. (2018). IoT standardization - Challenges, Perspectives and solution. Proceeding of the International Conference on Future Networks and Distributed Systems. June 26-27, 2018. Amman, Jordan. pp. 1-9. DOI: 10.1145/3231053.3231103
Sinha, P., Balas, B. Ostrovsky, Y., & Russell, R. (2006). Face recognition by humans: nineteen results all computer vision researchers should know about. Proceedings of the IEEE, 94(11), 1948-1962. DOI: 10.1109/JPROC.2006.884093.
Sun, J., Shen, Y., Yang, W., & Liao, Q. (2020). Classifier shared deep network with multi-hierarchy loss for low resolution face recognition. Signal Processing: Image Communication, 82, 1-8. DOI: 10.1016/j.image.2019.115766
Wojcik, W., Gromaszek, K., & Junisbekov, M. (2016). Face recognition - semisupervised classification, subspace projection and evaluation methods. London, UK: IntechOpen. DOI: 10.5772/62950
Yao, Y., & Li, C. (2015). Hand gesture recognition and spotting in uncontrolled environments based on classifier weighting. Proceeding of the IEEE International Conference on Image Processing. September 27-30, 2015. Quebec City, Canada. pp. 3082-3086. DOI: 10.1109/ICIP.2015.7351370
Zhao, X., Li, J., Liu, W., Zhang, J., & Li, Y. (2020). Design of the sleeping aid system based on face recognition. Ad Hoc Networks, 99, 1-10. DOI: 10.1016/j.adhoc.2019.102070
Zhu, J., Zheng, W., Lu, F., & Lai, J. (2017). Illumination invariant single face image recognition under heterogeneous lighting condition. Pattern Recognition, 66, 313-327. DOI: 10.1016/j.jvcir.2018.08.007.
Zou, W. W., & Yuen, P. C. (2012). Very low resolution face recognition problem. IEEE Transactions on Image Processing, 21(1), 327-340. DOI: 10.1109/TIP.2011.2162423