FACE DETECTION AND RECOGNITION USING HAAR CLASSIFIER AND LBP HISTOGRAM

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Indrajit Das
Indrasom Gangopadhyay
Anulekha Chatterjee

Abstract

Facial recognition technology is the process for identifying or verifying a face from digital images. The need for face recognition has been of real importance with the development of modern society. Detection and recognition of faces has been on the rise worldwide owing the requirement for security for economic transactions, authorization, national safety and security and other important factors. The technology comprises of face detection, database creation and face recognition. This paper presents a new approach of face identification using LBP method and Haar-like features. The first step is face detection which is done using Haar cascade classifier. After detection, a face is saved in the database. Then the faces from the database are passed through the face recognition algorithm. The Local Binary Pattern Histogram (LBPH) method is used for face recognition. The performance of face detection can be seen to produce maximum error of 1.6%, 2.1% and 0.8% in case of Real-Time video, image file and video file respectively which may be considered accurate. The recognition algorithm produces maximum error of 0.4% which may be considered accurate as well.

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Author Biographies

Indrajit Das, Meghnad Saha Institute of Technology, Department of Information Technology

Assistant Professor , Department of Information Technology

Indrasom Gangopadhyay, Meghnad Saha Institute of Technology, Department of Information Technology

Department of Information Technology

Anulekha Chatterjee, Meghnad Saha Institute of Technology, Department of Information Technology

Department of Information Technology

References

Z.Wang and F.Gao, “An Embedded Parallel Face Detection System Based on Multicore Processorâ€, IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2017, pp. 2684-2688, ISBN: 978-1-4673-8979-2.

P.Ji, Y. Kim, Y. Yang and Y.S.Kim, “Face Occlusion Detection Using Skin Color Ratio and LBP Features for Intelligent Video Surveillance Systemsâ€, Federated Conference on Computer Science and Information Systems (FedCSIS), 2016, pp. 253–259, ISBN: 978-8-3608-1090-3.

T.Li, W.Hou, F.Lyu, Y.Lei and C.Xiao, “Face Detection based-on Depth Information Using HOG-LBPâ€, 6th International Conference on Instrumentation & Measurement, Computer, Communication and Control, 2016, pp.779-784, ISBN: 978-1-5090-1195-7.

B.V.Thiyagarajan, A.Mayur, B.Ravina, and G.Akilesh, “LBP-Haar Multi-Feature Pedestrian Detection for Auto-Braking and Steering Control Systemâ€, International Conference on Computational Intelligence and Communication Networks (CICN), 2015, pp. 1527-1531, ISBN: 978-1-5090-0077-7.

S.A.A.M Faudzi and N.Yahya, “Evaluation of LBP – Based Face Recognition Techniquesâ€, International Conference on Intelligent and Advanced Systems (ICIAS), 2014, pp. 1-6, ISBN: 978-1-4799-4653-2.

C.Lin, J.T.Fu, S.H.Wang and C.Huang, “New Face Detection Method Based on Multi-Scale Histogramsâ€, IEEE 2nd International Conference on Multimedia Big Data, 2016, pp.229-232, ISBN: 978-1-5090-2179-6.

S.Lee, S.Jang, J.Kim and B.Choi, “A Hardware Architecture of Face Detection for Human-robot Interaction and its Implementationâ€, IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), 2016, pp. 1 -2 ISBN: 978-1-5090-2743-9.

S. Naveen, R.S. Fathima and R.S. Moni, “Face Recognition and Authentication using LBP and BSIF mask detection and Eliminationâ€, International Conference on Communication Systems and Networks (ComNet), International Conference, Thiruvananthapuram, India, 2016, pp.99-102, ISBN: 978-1-5090-3349-2.

Z.Jun, H. Jizhao, T.Zhenglan and W. Feng, “Face detection based on LBPâ€, 2017 IEEE 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), 2017, pp. 421-425, ISBN: 978-1-5090-5035-2.

Z.Liu, L.Lv and Y Wu, “Development of face Recognition System Based on PCA and LBP for Intelligent Anti-Theft Doorsâ€, 2nd IEEE International Conference Computer and Communications (ICCC), 2016, pp. 341-346, ISBN: 978-1-4673-9026-2.

Y.P. Chen, C.H. Liu , K. Y. Chou and S.Y. Wang, “Real-time and Low-memory Multi-face Detection System Design based on Naive Bayes Classifier using FPGAâ€, International Automatic Control Conference (CACS), 2016, pp. 7-12, ISBN: 978-1-5090-4109-1.

I.S.Z.Ugli and B.M.M. Ugli, “ Optimization detection on smiling and opening eyes in faces with algorithm LBPâ€, International Conference on Information Science and Communications Technologies (ICISCT),2016, pp. 1-4, 978-1-5090-3546-5.