Penguatan Ketepatan Pengenalan Wajah Viola-Jones Dengan Pelacakan
Abstract
Aplikasi pengenalan wajah sebagian besar berorientasi pada penguatan sistem keamanan dan pemantauan. Aplikasi-aplikasi tersebut banyak dikembangkan akibat adanya kajian penguatan ketepatan pengenalan wajah yang dikembangkan terus-menerus oleh peneliti. Variasi fitur wajah setiap orang yang kompleks dan perubahannya dari waktu ke waktu, bahkan dalam waktu yang singkat menjadikan optimalisasi ketepatan pengenalannya semakin rumit. Studi ini bertujuan untuk meningkatkan performa metode Viola-Jones pada target yang bergerak dengan integrasi algoritma tracking. Algoritma tracking yang diintegrasikan adalah algoritma Continuously Adaptive Mean Shift (Camshift). Algoritma ini merupakan pengembangan dari algoritma Mean Shift yang secara terus menerus melakukan adaptasi atau penyesuaian terhadap distribusi probabilitas warna yang selalu berubah tiap pergantian frame dari sebuah sequence video. Integrasi tracking dengan Viola-Jones signifikan meningkatkan ketepatan pengenalan wajah dibandingkan tanpa tracking yaitu sebesar 96%.
Downloads
References
Khattar, S., Sachdeva, A., Kumar, R., & Gupta, R. (2019). Smart Home With Virtual Assistant Using Raspberry Pi. Proceedings of the 9th International Conference On Cloud Computing, Data Science and Engineering, Confluence 2019, pp. 576—579. https://doi.org/10.1109/CONFLUENCE.2019.8776918
Munir, A., Kashif Ehsan, S., Mohsin Raza, S. M., & Mudassir, M. (2019). Face and Speech Recognition Based Smart Home. 2019 International Conference on Engineering and Emerging Technologies, ICEET 2019, pp. 1—5. https://doi.org/10.1109/CEET1.2019.8711849
Nguyen, T., Lakshmanan, B. & Sheng, W. (2019). A Smart Security System with Face Recognition A Smart Security System with Face Recognition. School of Electrical and Computer Engineering, Oklahoma State University.
Pawar, S., Kithani, V., Ahuja, S., & Sahu, S. (2018). Smart Home Security Using IoT and Face Recognition. Proceedings - 2018 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018, pp. 1—6. https://doi.org/10.1109/ICCUBEA.2018.8697695
Wati, D. A. R., & Abadianto, D. (2018). Design of Face Detection and Recognition System for Smart Home Security Application. Proceedings - 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2017, pp. 342—347. https://doi.org/10.1109/ICITISEE.2017.8285524
Abdullah, N. A., Saidi, M. J., Rahman, N. H. A., Wen, C. C., & Hamid, I. R. A. (2017). Face Recognition for Criminal Identification: an Implementation of Principal Component Analysis for Face Recognition for Criminal Identification : an Implementation of Principal Component Analysis for Face Recognition. AIP Conference Proceedings. https://doi.org/10.1063/1.5005335
Degadwala, S., Pandya, S., Patel, V., Shah, S., & Doshi, U. (2018). A Review on Real Time Face Tracking and Identification for Surveillance System. International Conference on Recent Trends in Engineering, Science & Technology - (ICRTEST 2016). https://doi.org/10.1049/cp.2016.1477
Holat, R., & Kulac, S. (2014). ID Identification by Using Face Detection and Recognition Systems. 22nd Signal Processing and Communications Applications Conference, SIU 2014 - Proceedings, pp.866—869. https://doi.org/10.1109/SIU.2014.6830367
Irianto, Suhendro, Zaini, T. M., & Karmila, S. (2013). Face Image Retrieval Approach for Criminal Identification in the DC Domain. World Academy of Science, Engineering and Technology, Vol. 82.
Kang, S., Lee, J., Kim, C., & Yoo, H. J. (2018). B-Face: 0.2 MW CNN-Based Face Recognition Processor with Face Alignment for Mobile User Identification. IEEE Symposium on VLSI Circuits, Digest of Technical Papers, 2018, pp. 137—138. https://doi.org/10.1109/VLSIC.2018.8502266
Kim, J., Yun, S. S., Kang, B. N., Kim, D., & Choi, J. (2017). Reliable Multi-Person Identification Using DCNN Based Face Recognition Algorithm and Scale-Ratio Method. 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017, pp. 97—101. https://doi.org/10.1109/URAI.2017.7992895
Son, H., Chi, S., & Kim, K. K. (2012). User Identification Using Face Recognition for Sports Simulator. 9th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2012, pp. 539—540. https://doi.org/10.1109/URAI.2012.6463067
Chowdhury, A., Atoum, Y., Tran, L., Liu, X., & Ross, A. (2018). MSU-AVIS Dataset: Fusing Face and Voice Modalities for Biometric Recognition in Indoor Surveillance Videos. Proceedings - International Conference on Pattern Recognition, pp. 3567—3573. https://doi.org/10.1109/ICPR.2018.8545260
Harikrishnan, J., Sudarsan, A., Sadashiv, A., & Remya Ajai, A. S. (2019). Vision-Face Recognition Attendance Monitoring System for Surveillance using Deep Learning Technology and Computer Vision. Proceedings-International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019, pp. 1—5. https://doi.org/10.1109/ViTECoN.2019.8899418
Kaliappan, J., Shreyansh, J., Shanmuga Sundari, P., & Singamsetti, M. S. (2019). Surveillance Camera Using Face Recognition for Automatic Attendance Feeder and Energy Conservation in Classroom. Proceedings-International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019, pp. 1—5. https://doi.org/10.1109/ViTECoN.2019.8899369
Mao, Y., Li, H., & Yin, Z. (2014). Who Missed The Class ? - Unifying Multi-Face Detection, Tracking And Recognition In Videos. 2014 IEEE International Conference on Multimedia and Expo (ICME).
Qezavati, H., Majidi, B., & Manzuri, M. T. (2019). Partially Covered Face Detection in Presence of Headscarf for Surveillance Applications. 4th International Conference on Pattern Recognition and Image Analysis, IPRIA 2019, pp. 195—199. https://doi.org/10.1109/PRIA.2019.8786004
Beham, M. P., & Roomi, S. M. M. (2013). A Review of Face Recognition Methods. International Journal of Pattern Recognition and Artificial Intelligence, Vol. 27(04), 1356005. https://doi.org/10.1142/s0218001413560053
Dubey, S. R., & Mukherjee, S. (2018). A Multi-Face Challenging Dataset for Robust Face Recognition. 2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018, pp. 168—173. https://doi.org/10.1109/ICARCV.2018.8581283
Kumar, A., Kaur, A., & Kumar, M. (2019). Face Detection Techniques: a Review. Artificial Intelligence Review, Vol. 52(2), pp. 927—948. https://doi.org/10.1007/s10462-018-9650-2
Lal, M., Kumar, K., Arain, R. H., Maitlo, A., Ruk, S. A., & Shaikh, H. (2018). Study of Face Recognition Techniques: A Survey. International Journal of Advanced Computer Science and Applications, Vol. 9(6), pp. 42—49. https://doi.org/10.14569/IJACSA.2018.090606
Little, A. C., Jones, B. C., & Debruine, L. M. (2011). The Many Faces of Research on Face Perception. Philosophical Transactions of the Royal Society B: Biological Sciences, Vol. 366(1571), pp. 1634—1637. https://doi.org/10.1098/rstb.2010.0386
Wójcik, W., Gromaszek, K., & Junisbekov, M. (2016). Face Recognition: Issues, Methods and Alternative Applications. In Face Recognition - Semisupervised Classification, Subspace Projection and Evaluation Methods. https://doi.org/10.5772/62950
Jaiswal, S., Bhadauria, S. S., & Jadon, R. S. (2011). Evaluation of Face Recognition Methods. Journal of Global Research in Computer Science, Vol. 2(7).
Zarei, S. (2018). Face Recognition Methods Analysis. International Journal Artificial Intelligent and Informatics, Vol 1(1). https://doi.org/10.33292/ijarlit.v1i1.13
An, L., Bhanu, B., & Yang, S. (2012). Face Recognition in Multi-Camera Surveillance Videos. Proceedings-International Conference on Pattern Recognition, pp. 2885—2888.
Boontua, M., Nam-Asa, P., Arwatchananukul, S., & Aunsri, N. (2018). A Study of Features and Classifiers for Multiple Environment Face Recognition System. 3rd International Conference on Digital Arts, Media and Technology, ICDAMT 2018, Vol. 1(1), pp. 249—253. https://doi.org/10.1109/ICDAMT.2018.8376533
Kim, H.-I., & Ro, Y. M. (2016). Collaborative Facial Color Feature Learning Of Multiple Color Spaces For Face Recognition. IEEE International Conference on Image Processing (ICIP).
Wang, D., & Liu, L. (2015). Face Recognition in Complex Background: Developmental Network and Synapse Maintenance. International Journal of Smart Home, Vol. 9(10), pp. 47—62. https://doi.org/10.14257/ijsh.2015.9.10.06
Wang, D., Zheng, G., & Liu, L. (2015). Developmental Network and Its Application to Face Recognition. Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015, pp. 6310—6315. https://doi.org/10.1109/CCDC.2015.7161951
Mantoro, T., Ayu, M. A., & Suhendi. (2018). Multi-Faces Recognition Process Using Haar Cascades and Eigenface Methods. International Conference on Multimedia Computing and Systems-Proceedings, pp. 1—5. https://doi.org/10.1109/ICMCS.2018.8525935
Chinchu, S., Mohammed, A., & Mahesh, B. S. (2018). A Novel Method for Real Time Face Spoof Recognition for Single and Multiple User Authentication. 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2017, pp. 376—380. https://doi.org/10.1109/ICICICT1.2017.8342593
Sameem, M. S. I., Qasim, T., & Bakhat, K. (2016). Real Time Recognition of Human Faces. ICOSST 2016 - 2016 International Conference on Open Source Systems and Technologies Proceedings, pp. 62—65. https://doi.org/10.1109/ICOSST.2016.7838578
Boyko, N. (2018). Performance Evaluation and Comparison of Software for Face Recognition , based on Dlib and Opencv Library. 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), pp. 478—482.
Saha, R., Bhattacharjee, D., & Barman, S. (2014). Comparison of Different Face Recognition Method Based On PCA. International Journal Of Management & Information Technology, Vol. 10(4), pp. 2016—2022.
Parmar, D. N., & Mehta, B. B. (2014). Face Recognition Methods & Applications. International Journal of Computer Applications in Technology, Vol 4(1), pp. 83—86. Retrieved from http://arxiv.org/abs/1403.0485
Florestiyanto, M. Y., Himawan, H., & Silitonga, P. D. P. (2020). Improved Viola-Jones Face Recognition Using Tracking. TEST Engineering & Management, Vol. 83, pp. 3945—3952.
Huang, C., & Wei, N. (2012). Comparison And Prospect Study On Multiple Face Detection And Recognition Methods. Proceedings - 2012 International Conference on Computer Science and Service System, CSSS 2012, pp. 1619—1622. https://doi.org/10.1109/CSSS.2012.405
Sharma, R., & Patterh, M. S. (2015). A New Pose Invariant Face Recognition System using PCA and ANFIS. Optik - International Journal for Light and Electron Optics, Vol. 126(23), pp. 3483—3487. https://doi.org/10.1016/j.ijleo.2015.08.205
Von Söhsten, D., & Murilo, S. (2014). Multiple Face Recognition in Real-Time Using Cloud Computing, Emgu CV and Windows Azure. International Conference on Intelligent Systems Design and Applications, ISDA, pp. 137-140. https://doi.org/10.1109/ISDA.2013.6920723
Shamia, D., & Chandy, D. A. (2018). Analyzing the Performance of Viola Jones Face Detector on the LDHF database. Proceedings of IEEE International Conference on Signal Processing and Communication, ICSPC 2017, pp. 312—315. https://doi.org/10.1109/CSPC.2017.8305860
Chen, D., Jin, G., Lu, L., Tan, L., & Wei, W. (2018). Infrared Image Vehicle Detection Based on Haar-like Feature. Proceedings of 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference, pp. 662—667. https://doi.org/10.1109/IAEAC.2018.8577211
Copyright (c) 2020 Teknika
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
