Abstract
Human face recognition is distinguished by a method of identifying facts or confirmation that tests personality. The technique essentially relies on two stages, one is face identification, and another is face recognition. Facial recognition applies to a PC device with a few implementations in which human faces can be identified in pictures. Usually, facial identification is achieved by using “right” data from full-frontal facial photographs. Although there are a variety of situations in which full frontal faces are not visible, blemished faces captured by CCTV cameras are an excellent demonstration. Subsequently, the use of fractional facial data as tests is still, to a large extent, an unexplored field of research on the PC-based face recognition problem. In this research, through using incomplete facial evidence to concentrate on face recognition. By implementing critical analysis to evaluate the presentation of AI using the Haar Cascade Classifier is proposed and used to build our framework. There are three phases of the proposed face detection method such as the face data gathering (FDG) process, train the stored image (TSI) phase, face recognition using the local (FRUL) binary patterns histograms (LBPH) algorithm, and this classifier computation was tested by splitting it into four phases. In this analysis, Haar feature selection is applied to complete the detection phase, and also to generate an integral image, Adaboost preparing, Cascading Classifiers. To complete this venture's human protection facial recognition framework with face detection, local binary patterns histograms (LBPH) is used to estimate the model. In LBPH, a few parameters are used and a dataset is obtained by implementing an algorithm. By adding the LBPH operation and extracting the histograms, I got the Final computational part. “Image Processing Based Human Face Recognition Using Haar Cascade Classifier” Image Processing-Based Human Face Recognition Using Haar Cascade Classifier.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Manoharan S (2019) Smart image processing algorithm for text recognition, information extraction and vocalization for the visually challenged. J Innov Image Process (JIIP) 1(01):31–38
Kumar A, Kaur A, Kumar M (2018) Face detection techniques: a review. Springer Nature B.V. https://doi.org/10.1007/s10462-018-9650-2
Maglogiannis I, Vouyioukas D, Aggelopoulos C Face detection and recognition of natural human emotion using Markov random fields. https://doi.org/10.007/s0077900701650
Marciniak T, Chielewska A, Wechan R, Parzych M, Dabrowski A Influence of low resolution of images on reliability of face detection and recognition. https://doi.org/10.1007/s1104201315688
Zhi H, Lui S (2018) Face recognition based on genetic algorithm. J Vis Commun Image R. https://doi.org/10.1016/j.jvcir.2018.12.012
Manoharan S (2019) Image detection, classification and recognition for leak detection in automobiles. J Innov Image Process (JIIP) 1(02):61–70
Tofighi A, Monadjemi SA Face detection and recognition using skin color and Adaboost algorithm combined with Gabour features and SVM classifier. 1
Savvides et al Dynamic feature matching (DFM) for partial face recognition. https://doi.org/10.1109/TIP.2018.2870946
Shamrat FMJM, Allayear SM, Alam MF, Jabiullah MI, Ahmed R (2019) A smart embedded system model for the AC automation with temperature prediction. In: Singh M, Gupta P, Tyagi V, Flusser J, Ören T, Kashyap R (eds) Advances in computing and data sciences. ICACDS 2019. Communications in computer and information science, vol 1046. Springer, Singapore. https://doi.org/10.1007/978-981-13-9942-8_33
Karim A, Azam S, Shanmugam B, Kannoorpatti K (2020) Efficient clustering of emails into spam and ham: the foundational study of a comprehensive unsupervised framework. IEEE Access 8:154759–154788. https://doi.org/10.1109/ACCESS.2020.3017082
Liang C, Shanmugam B, Azam S, Karim A et al (2020) Intrusion detection system for the Internet of Things based on blockchain and multi-agent systems. Electronics 9(7):1120. https://doi.org/10.3390/electronics9071120
Haar Cascade Classifier Image. https://www.google.com/search?q=haar+cascade+classifier+image&source=lnms&tbm=isch&sa=X&v
Shamrat FMJM, Tasnim Z, Nobel NI, Ahmed MdR (2019) An automated embedded detection and alarm system for preventing accidents of passengers vessel due to overweight. In: Proceedings of the 4th international conference on big data and Internet of Things (BDIoT'19). Association for Computing Machinery, New York, NY, USA, Article 35, pp 1–5. https://doi.org/10.1145/3372938.3372973
Shamrat FMJM, Nobel NI, Tasnim Z, Ahmed R (2020) Implementation of a smart embedded system for passenger vessel safety. In: Saha A, Kar N, Deb S (eds) Advances in computational intelligence, security and Internet of Things. ICCISIoT 2019. Communications in computer and information science, vol 1192. Springer, Singapore. https://doi.org/10.1007/978-981-15-3666-3_29
Ahmed MdR, Ali MdA, Ahmed N, Zamal MdFB, Shamrat FMJM (2020) The impact of software fault prediction in real-world application: an automated approach for software engineering. In: Proceedings of 2020 the 6th international conference on computing and data engineering (ICCDE 2020). Association for Computing Machinery, New York, NY, USA, pp 247–251. https://doi.org/10.1145/3379247.3379278
Shamrat FMJM, Tasnim Z, Ghosh P, Majumder A, Hasan MZ (2020) Personalization of job circular announcement to applicants using decision tree classification algorithm. In: 2020 IEEE international conference for innovation in technology (INOCON), Bangluru, pp 1-5. https://doi.org/10.1109/INOCON50539.2020.9298253
Karim MA, Karim A, Azam S, Ahmed E, Boer FD, Islam A, Nur FN (2021) Cognitive learning environment and classroom analytics (CLECA). Innovative data communication technologies and application (IDCTA), vol 59. Springer
Shamrat FMJM, Asaduzzaman Md, Rahman AKMS, Tusher RTH, Tasnim Z (2019) A comparative analysis of Parkinson disease prediction using machine learning approaches. Int J Sci Technol Res 8(11):2576–2580. ISSN 2277-8616
Rahman AKMS, Shamrat FMJM, Tasnim Z, Roy J, Hossain SA (2019) A comparative study on liver disease prediction using supervised machine learning algorithms. Int J Sci Technol Res 8(11):419–422. ISSN 2277-8616
Shamrat FMJM, Raihan MdA, Rahman AKMS, Mahmud I, Akter R (2020) An analysis on breast disease prediction using machine learning approaches. Int J Sci Technol Res 9(2):2450–2455. ISSN 2277-8616
Shamrat FMJM, Asaduzzaman Md, Ghosh P, Sultan MdD, Tasnim Z (2020) A web based application for agriculture: “smart farming system”. Int J Emerg Trends Eng Res 8(6):2309–2320. ISSN 2347-3983, https://doi.org/10.30534/ijeter/2020/18862020
Kathed A, Azam S, Shanmugam B, Karim A, Yeo KC, De Boer F, Jonkman M (2019) An enhanced 3-tier multimodal biometric authentication. In: 2019 international conference on computer communication and informatics (ICCCI). IEEE, pp 1–6. https://doi.org/10.1109/ICCCI.2019.8822117
Shamrat FMJM, Tasnim Z, Mahmud I, Jahan N, Nobel NI (2020) Application of k-means clustering algorithm to determine the density of demand of different kinds of jobs. Int J Sci Technol Res 9(2):2550–2557. ISSN 2277-8616
Shamrat FMJM, Ghosh P, Sadek MH, Kazi MA, Shultana S (2020) Implementation of machine learning algorithms to detect the prognosis rate of kidney disease. In: 2020 IEEE international conference for innovation in technology (INOCON), Bangluru, pp 1–7. https://doi.org/10.1109/INOCON50539.2020.9298026
Shamrat FMJM, Tasnim Z, Rahman AKMS, Nobel NI, Hossain SA (2020) An effective implementation of web crawling technology to retrieve data from the world wide web (www). Int J Sci Technol Res 9(1):1252–1256. ISSN 2277-8616
Ghosh P et al Efficient prediction of cardiovascular disease using machine learning algorithms with relief and LASSO feature selection techniques. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3053759
Shamrat FMJM, Mahmud I, Rahman AKMS, Majumder A, Tasnim Z, Nobel NI (2020) A smart automated system model for vehicles detection to maintain traffic by image processing. Int J Sci Technol Res 9(2):2921–2928. ISSN 2277-8616
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Javed Mehedi Shamrat, F.M., Majumder, A., Antu, P.R., Barmon, S.K., Nowrin, I., Ranjan, R. (2022). Human Face Recognition Applying Haar Cascade Classifier. In: Ranganathan, G., Bestak, R., Palanisamy, R., Rocha, Á. (eds) Pervasive Computing and Social Networking. Lecture Notes in Networks and Systems, vol 317. Springer, Singapore. https://doi.org/10.1007/978-981-16-5640-8_12
Download citation
DOI: https://doi.org/10.1007/978-981-16-5640-8_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-5639-2
Online ISBN: 978-981-16-5640-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)