Machine learning models in people detection and identification : a literature review
Servicio Nacional de Aprendizaje, Centro CIES
email: cvnino2@misena.edu.co
Universidad Francisco de Paula Santander, Facultad de Ingeniería, Programa Académico de Ingeniería Electromecánica. CvLAC: https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do? cod_rh=0000304603
email: yeseniarestrepo@ufps.edu.co
Universidad Francisco de Paula Santander, Facultad de Ingeniería, Programa Académico de Ingeniería Electrónica. CvLAC: https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do? cod_rh=0001132598
email: sergio.castroc@ufps.edu.co
Introduction: This article is the result of research entitled "Development of a prototype to optimize access conditions to the SENA-Pescadero using artificial intelligence and open-source tools", developed at the Servicio Nacional de Aprendizaje in 2020.
Problem: How to identify Machine Learning Techniques applied to computer vision processes through a literature review?
Objective: Determine the application, as well as advantages and disadvantages of machine learning techniques focused on the detection and identification of people.
Methodology: Systematic literature review in 4 high-impact bibliographic and scientific databases, using search filters and information selection criteria.
Results: Machine Learning techniques defined as Principal Component Analysis, Weak Label Regularized Local Coordinate Coding, Support Vector Machines, Haar Cascade Classifiers and EigenFaces and FisherFaces, as well as their applicability in detection and identification processes.
Conclusion: The research led to the identification of the main computational intelligence techniques based on machine learning, applied to the detection and identification of people. Their influence was shown in several application cases, but most of them were focused on the implementation and optimization of access control systems, or tasks in which the identification of people was required for the execution of processes.
Originality: Through this research, we studied and defined the main machine learning techniques currently used for the detection and identification of people.
Limitations: The systematic review is limited to information available in the 4 databases consulted, and the amount of information is variable as articles are deposited in the databases.
E. Chian, W. Fang, Y. M. Goh, and J. Tian, “Computer vision approaches for detecting missing barricades,” Autom. Constr., vol. 131, no. April, p. 103862, 2021. doi: 10.1016/j.autcon.2021.103862
C. S. Sanoj, N. Vijayaraj, and D. Rajalakshmi, “Vision approach of human detection and tracking using focus tracing analysis,” in 2013 International Conference on Information Communication and Embedded Systems, ICICES 2013. doi: 10.1109/ICICES.2013.6508394
V. Fremont, M. T. Bui, D. Boukerroui, and P. Letort, “Vision-based people detection system for heavy machine applications,” Sensors (Switzerland), vol. 16, no. 1, pp. 1–30, 2016. doi: 10.3390/s16010128
D. Yacchirema, J. S. de Puga, C. Palau, and M. Esteve, “Fall detection system for elderly people using IoT and ensemble machine learning algorithm,” Pers. Ubiquitous Comput., vol. 23, no. 5–6, pp. 801–817, 2019. doi: 10.1007/s00779-018-01196-8
M. Mariappan, L. K. Thong, and K. Muthukaruppan, “A design methodology of an embedded motion-detecting video surveillance system,” Int. J. Integr. Eng., vol. 12, no. 2, pp. 55–69, 2020.
B. Garcia-Garcia, T. Bouwmans, and A. J. Rosales Silva, “Background subtraction in real applications: Challenges, current models and future directions,” Comput. Sci. Rev., vol. 35, p. 100204, 2020. doi: 10.1016/j.cosrev.2019.100204
L. Neumann and A. Vedaldi, “Tiny People Pose,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11363 LNCS, pp. 558–574, 2019. doi: 10.1007/978-3-030-20893-6_35
S. Sivaranjani and S. Sumathi, “A review on implementation of bimodal newborn authentication using raspberry Pi,” in Global Conference on Communication Technologies, GCCT 2015, 2015, no. Gcct, pp. 267–272. doi: 10.1109/GCCT.2015.7342664.
B. Abade, D. P. Abreu, and M. Curado, “A non-intrusive approach for indoor occupancy detection in smart environments,” Sensors (Switzerland), vol. 18, no. 11, pp. 1–18, 2018. doi: 10.3390/s18113953
S. Karlos, N. Fazakis, S. Kotsiantis, and K. Sgarbas, “A Semisupervised Cascade Classification Algorithm,” Appl. Comput. Intell. Soft Comput., vol. 2016, pp. 1–14, 2016. doi: 10.1155/2016/5919717
W. Casaca, D. P. Ederli, E. Silva, F. P. Baixo, T. G. Godoy, and M. Colnago, “Comparing the Performance of Mathematical Morphology and Bhattacharyya Distance for Airport Extraction,” in International Geoscience and Remote Sensing Symposium (IGARSS), 2020, pp. 529–532. doi: 10.1109/IGARSS39084.2020.9323733
R. Cheripelli and K. R. Sri, “Evaluation of machine Learning Models for Credit Scoring,” Ingeniería Solidaria, vol. 16, no. 2798, pp. 2798–2805, 2020. doi: 10.16925/2357-6014.2020.01.11
J. Andrew Bagnell, “Reinforcement Learning in Robotics: A Survey,” Springer Tracts Adv. Robot., vol. 97, no. 1, pp. 9–67, 2014. doi: 10.1177/0278364913495721
R. Agrawal, “Predictive Analysis Of Breast Cancer Using Machine Learning Techniques,” Ingeniería Solidaria, vol. 15, no. 29, pp. 1–23, 2019. doi: 10.16925/2357-6014.2019.03.01
A. Gupta and A. Barbu, “Parameterized principal component analysis,” Pattern Recognit., vol. 78, pp. 215–227, 2018. doi: 10.1016/j.patcog.2018.01.018
A. Mishra, N. Modi, and M. Panda, “Biometric Identification (Analysis Based on Fingerprints and Faces),” Adv. Intell. Syst. Comput., vol. 563, pp. 27–38, 2018. doi: 10.1007/978-981-10-6872-0_3
A. Lasisi and N. Attoh-Okine, “Principal components analysis and track quality index: A machine learning approach,” Transp. Res. Part C Emerg. Technol., vol. 91, no. April 2018, pp. 230–248, 2018. doi: 10.1016/j.trc.2018.04.001
V. Chawda, V. Arya, S. Pandey, Shristi, and M. Valleti, “Unique Face Identification System using Machine Learning,” in Proceedings of the 2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020, 2020, pp. 701–706. doi: 10.1109/ICIRCA48905.2020.9182981
A. Halder, A. Jati, G. Singh, A. Konar, A. Chakraborty, and R. Janarthanan, “Facial action point based emotion recognition by principal component analysis,” Adv. Intell. Soft Comput., vol. 131 AISC, no. VOL. 2, pp. 721–733, 2012. doi: 10.1007/978-81-322-0491-6_66
S. Sharma, M. Bhatt, and P. Sharma, “Face recognition system using machine learning algorithm,” in Proceedings of the 5th International Conference on Communication and Electronics Systems, ICCES 2020, 2020, no. Icces, pp. 1162–1168. doi: 10.1109/ICCES48766.2020.9137850
J. B. Li and H. Gao, “Sparse data-dependent kernel principal component analysis based on least squares support vector machine for feature extraction and recognition,” Neural Comput. Appl., vol. 21, no. 8, pp. 1971–1980, 2012. doi: 10.1007/s00521-011-0600-z
K. G. Shanthi, S. Sesha Vidhya, K. Vishakha, S. Subiksha, K. K. Srija, and R. Srinee Mamtha, “Algorithms for face recognition drones,” Mater. Today Proc., vol. X, no. 12, pp. 1–4, 2021. doi: 10.1016/j.matpr.2021.06.186
N. Bakshi and V. Prabhu, “Face recognition system for access control using principal component analysis,” in ICCT 2017 - International Conference on Intelligent Communication and Computational Techniques, 2018, vol. January, pp. 145–150. doi: 10.1109/INTELCCT.2017.8324035
Y. Zhu, C. Zhu, and X. Li, “Improved principal component analysis and linear regression classification for face recognition,” Signal Processing, vol. 145, pp. 175–182, 2018. doi: 10.1016/j.sigpro.2017.11.018
S. Saleem, J. Shiney, B. Priestly Shan, and V. Kumar Mishra, “Face recognition using facial features,” Mater. Today Proc., 2021. doi: 10.1016/j.matpr.2021.07.402
X. Fang, Y. Xu, X. Li, Z. Lai, and W. K. Wong, “Learning a nonnegative sparse graph for linear regression,” IEEE Trans. Image Process., vol. 24, no. 9, pp. 2760–2771, 2015. doi: 10.1109/TIP.2015.2425545
B. C. Chen, C. S. Chen, and W. H. Hsu, “Cross-age reference coding for age-invariant face recognition and retrieval,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8694 LNCS, pp. 768–783, 2014. doi: 10.1007/978-3-319-10599-4_49
D. Wang, S. C. H. Hoi, and Y. He, “A unified learning framework for auto face annotation by mining web facial images,” ACM Int. Conf. Proceeding Ser., vol. 10, pp. 1392–1401, 2012. doi: 10.1145/2396761.2398444
D. Wang, S. C. H. Hoi, Y. He, J. Zhu, T. Mei, and J. Luo, “Retrieval-based face annotation by weak label regularized local coordinate coding,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 3, pp. 550–563, 2014. doi: 10.1109/TPAMI.2013.145
D. Sakharkar and S. Bodkhe, “A study on various face detection techniques in real time video environment,” 2015 Int. Conf. Pervasive Comput. Adv. Commun. Technol. Appl. Soc. ICPC 2015, vol. 00, no. c, pp. 3–6, 2015. doi: 10.1109/PERVASIVE.2015.7086990
C. Nandhagopal and K. Priyanka, “A comparative analysis of face annotation schemes for web facial images in social networks,” in Proceedings of 2015 IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2015, 2015, pp. 1–5. doi: 10.1109/ICECCT.2015.7226127
S. Kim, B. M. Mun, and S. J. Bae, “Data depth based support vector machines for predicting corporate bankruptcy,” Appl. Intell., vol. 48, no. 3, pp. 791–804, 2018. doi: 10.1007/s10489-017-1011-3
B. Cyganek, B. Krawczyk, and M. Woźniak, “Multidimensional data classification with chordal distance based kernel and Support Vector Machines,” Eng. Appl. Artif. Intell., vol. 46, pp. 10–22, 2015. doi: https://doi.org/10.1016/j.engappai.2015.08.001
S. Sun, X. Xie, and C. Dong, “Multiview Learning With Generalized Eigenvalue Proximal Support Vector Machines,” IEEE Trans. Cybern., vol. 49, no. 2, pp. 688–697, 2019. doi: 10.1109/TCYB.2017.2786719
J. S. Sartakhti, H. Afrabandpey, and N. Ghadiri, “Fuzzy least squares twin support vector machines,” Eng. Appl. Artif. Intell., vol. 85, no. June, pp. 402–409, 2019. doi: 10.1016/j.engappai.2019.06.018
J. K. J. Julina and T. Sree Sharmila, “Facial recognition using histogram of gradients and support vector machines,” in International Conference on Computer, Communication, and Signal Processing: Special Focus on IoT, ICCCSP 2017, 2017. doi: 10.1109/ICCCSP.2017.7944082
C. Rayani and K. Rajakumar, “Face detection and recognition using support vector machine,” Int. J. Eng. Adv. Technol., vol. 8, no. 4, pp. 382–384, 2019. doi: 10.31979/etd.wg5s-gyqn
H. Chen, A. C. Gallagher, and B. Girod, “The hidden sides of names - Face modeling with first name attributes,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 9, pp. 1860–1873, 2014. doi: 10.1109/TPAMI.2014.2302443
A. Gavriilidis, J. Velten, S. Tilgner, and A. Kummert, “Machine learning for people detection in guidance functionality of enabling health applications by means of cascaded SVM classifiers,” J. Franklin Inst., vol. 355, no. 4, pp. 2009–2021, 2018. doi: 10.1016/j.jfranklin.2017.10.008
L. Shmaglit and V. Khryashchev, “Gender classification of human face images based on adaptive features and support vector machines,” Opt. Mem. Neural Networks (Information Opt., vol. 22, no. 4, pp. 228–235, 2013. doi: 10.3103/S1060992X13040036
L. L. Chambino, J. S. Silva, and A. Bernardino, “Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units,” Sensors (Basel)., vol. 21, no. 13, pp. 1–15, 2021. doi: 10.3390/s21134520
C. Campomanes-Alvarez, B. R. Campomanes-Alvarez, and P. Quiros, “Person Identification System in a Platform for Enabling Interaction with Individuals Affected by Profound and Multiple Learning Disabilities,” in Proceedings of 2019 IEEE 18th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2019, 2019. doi: 10.1109/ICCICC46617.2019.9146032
M. Abdulrahman and A. Eleyan, “Facial expression recognition using Support Vector Machines,” in 2015 23rd Signal Processing and Communications Applications Conference, SIU 2015 - Proceedings, 2015, vol. 5, pp. 276–279. doi: 10.1109/SIU.2015.7129813
M. Arafah, A. Achmad, Indrabayu, and I. Sari-Areni, “Face Recognition for Wearing a Veil Case using Histogram of Oriented Gradients,” ICIC Express Lett., vol. 12, no. 4, pp. 1–10, 2021.
I. P. Adegun and H. B. Vadapalli, “Facial micro-expression recognition: A machine learning approach,” Sci. African, vol. 8, no. e00465, pp. 1–14, 2020. doi: 10.1016/j.sciaf.2020.e00465
D. Dixit, S. Parashar, A. Gondalia, A. Sengupta, and M. Sivigami, “Facial identification using Haar Cascading with BRISK,” in 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), 2020. doi: 10.1109/ic-ETITE47903.2020.432
A. P. Atmaja, S. B. Setyawan, L. D. Setia, S. V. Yulianto, B. Winarno, and T. Lestariningsih, “Face Recognition System using Micro Unmanned Aerial Vehicle,” J. Phys. Conf. Ser., vol. 1845, no. 1, pp. 1–7, 2021. doi: 10.1088/1742-6596/1845/1/012043
V. D. A. Kumar, V. D. A. Kumar, S. Malathi, K. Vengatesan, and M. Ramakrishnan, “Facial Recognition System for Suspect Identification Using a Surveillance Camera,” Pattern Recognit. Image Anal., vol. 28, no. 3, pp. 410–420, 2018. doi: 10.1134/S1054661818030136
H. Zunair, O. Maniha, and M. J. Kabir, “Design and Implementation of an Automated Multi-Functional Attendance System with Real Time Web Visualization,” in 2018 2nd International Conference on Smart Sensors and Application, ICSSA 2018, 2018. doi: 10.1109/ICSSA.2018.8535928
K. S. Gautam and S. K. Thangavel, “Video analytics-based intelligent surveillance system for smart buildings,” Soft Comput., vol. 23, no. 8, pp. 2813–2837, 2019. doi: 10.1007/s00500-019-03870-2
B. Rehman, W. H. Ong, A. C. H. Tan, and T. D. Ngo, “Face detection and tracking using hybrid margin-based ROI techniques,” Vis. Comput., vol. 36, no. 3, pp. 633–647, 2020. doi: 10.1007/s00371-019-01649-y
J. D. W. S. Souza, S. Jothi, and A. Chandrasekar, “Automated Attendance Marking and Management System by Facial Recognition Using Histogram,” in 2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019, 2019. doi: 10.1109/ICACCS.2019.8728399
V. Wati, K. Kusrini, H. Al Fatta, and N. Kapoor, “Security of facial biometric authentication for attendance system,” Multimed. Tools Appl., vol. 80, no. 15, pp. 23625–23646, 2021. doi: 10.1007/s11042-020-10246-4
G. M. Zafaruddin and H. S. Fadewar, “Face recognition using eigenfaces,” Adv. Intell. Syst. Comput., vol. 810, pp. 855–864, 2018. doi: 10.1007/978-981-13-1513-8_87
K. C. Kirana, S. Wibawanto, and H. W. Herwanto, “Emotion recognition using fisher face-based viola-jones algorithm,” in International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2018, pp. 173–177. doi: 10.1109/EECSI.2018.8752783
M. üg. Çarıkçı and F. Özen, “A Face Recognition System Based on Eigenfaces Method,” Procedia Technol., vol. 1, no. 12, pp. 118–123, 2012. doi: 10.1016/j.protcy.2012.02.023
M. Leo, F. Battisti, M. Carli, and A. Neri, “Face retrieval in video sequences using Web images database,” Image Process. Algorithms Syst. XIII, vol. 9399, no. 93990Y, pp. 1–6, 2015. doi: 10.1117/12.2083316
J. Zhao, Z. Zhou, and F. Cao, “Human face recognition based on ensemble of polyharmonic extreme learning machine,” Neural Comput. Appl., vol. 24, no. 6, pp. 1317–1326, 2014. doi: 10.1007/s00521-013-1356-4
M. R. D. Rodavia, O. Bernaldez, and M. Ballita, “Web and mobile based facial recognition security system using Eigenfaces algorithm,” in Proceedings of 2016 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2016, 2017, no. December, pp. 86–92. doi: 10.1109/TALE.2016.7851776
M. A. P. Chamikara, P. Bertok, I. Khalil, D. Liu, and S. Camtepe, “Privacy Preserving Face Recognition Utilizing Differential Privacy,” Comput. Secur., vol. 97, no. 6, p. 101951, 2020. doi: 10.1016/j.cose.2020.101951
C. Niu and L. He, “Research on athlete recognition based on image feature extraction and artificial intelligence classification,” J. Ambient Intell. Humaniz. Comput., vol. April, no. 0123456789, pp. 1–13, 2021. doi: 10.1007/s12652-021-03152-6
H. Liu et al., “Development of a Face Recognition System and Its Intelligent Lighting Compensation Method for Dark-Field Application,” IEEE Trans. Instrum. Meas., vol. 70, no. 12, pp. 1–16, 2021. doi: 10.1109/TIM.2021.3111076
G. A. Kukharev and N. L. Shchegoleva, “Algorithms of Two-Dimensional Projection of Digital Images in Eigensubspace: History of Development, Implementation and Application,” Pattern Recognit. Image Anal., vol. 28, no. 2, pp. 185–206, 2018. doi: 10.1134/S1054661818020116
G. Cheng and Z. Song, “Robust face recognition based on space,” Optik (Stuttg)., vol. 125, no. 12, pp. 2804–2808, 2014. doi: 10.1016/j.ijleo.2013.11.042
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