Skip to main content
SpringerLink
Log in

RETRACTED ARTICLE: An efficient human face verification system based on ELBP: a high precision feature

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

This article was retracted on 14 June 2022

This article has been updated

Abstract

Human Face Verification (HFV) is used for distinguishing the human as an authorized or unauthorized Person in security and biometric related applications. HFV has huge applications in many areas. HFV process involves the collection, detection, pre-processing, extraction, classifying and Verification stages. The main problem in HFV system is to detect the faces automatically from front view with variation in illumination, expression, and occlusion and so on. This work is mainly focused on extraction and recognition stages. LBP is widely used method for texture analysis. LBP usually compares the center pixel to its neighbors over a fixed radius. LBP is generally sensitive to noise. In order to overcome this problem ELBP method had been proposed which deals with the rotation invariant of face images. The proposed method is evaluated for noise environment, pose variations and emotions. For each one, the experimental results are tabulated and analyzed. From the result analysis, the proposed method provides better accuracy than existing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Change history

References

  • Ahonen T, Member S, Hadid A, Pietikinen M, Member S (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28:2037–2041

    Article  Google Scholar 

  • Aoun NB, Mejdoub M, Amar CB (2014) Graph-based approach for human action recognition using spatio-temporal features. J Vis Commun Image Represent 25:329–338

    Article  Google Scholar 

  • Bhagya C, Shyna A (2019) An overview of deep learning based object detection techniques. In: 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), pp: 1-6.

  • Borgi MA, Labate D, El’Arbi M, Amar CB (2013) Shearlet network-based sparse coding augmented by facial texture features for face recognition. In: International conference on image analysis and processing, pp: 611–620.

  • Chen W, Er MJ, Wu S (2006) Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans Syst Man Cyber B 36(2):458–466

    Article  Google Scholar 

  • Chihaoui M, Elkefi A, Bellil W, Amar CB (2016) A Survey of 2D face recognition techniques. Computers 5(4):28

    Article  Google Scholar 

  • Cho SI, Kang SJ (2018) Real-time people counting system for customer movement analysis. IEEE Access 6:55264–55272

    Article  Google Scholar 

  • Dammak M, Mejdoub M, Zaied M, Amar CB (2012) Feature vector approximation based on wavelet network. In: Proceedings of the 4th International Conference on Agents and Artificial Intelligence, Vilamoura, Portugal, pp: 394–399.

  • Do TT, Doan KN, Le TH, Le BH (2009) Boosted of Haar-like features and local binary pattern based face detection. In: 2009 IEEE-RIVF International Conference on Computing and Communication Technologies, pp: 1–8.

  • Duda RO, Hart PE, Stork DG (2001) Pattern classification. John Wiley and Sons, New York

    MATH  Google Scholar 

  • El’Arbi M, Amar CB, Nicolas H (2006) Video watermarking based on neural networks. In: Proceedings of the 2006 IEEE International Conference on Multimedia and Expo, Toronto, ON, Canada, pp: 1577–1580.

  • Hafiane A et al (2007) Median binary pattern for textures classification. Image analysis and recognition. Springer, Berlin, pp 387–398

    Google Scholar 

  • Jian Y, David Z, Alejandro F, Yang JY (2004) Two-dimensional PCA: a new approach to appearance based face representation and recognition. IEEE T Pattern Anal 26(1):131–137

    Article  Google Scholar 

  • Jin H, Liu Q, Lu H, Tong X (2004) Face detection using improved lbp under bayesian framework. In: Proceedings of Third International Conference on Image and Graphics, pp: 306–309.

  • KambiBeli IL, Guo C (2017) Enhancing face identification using local binary patterns and K-nearest neighbors. J Imaging 3:37

    Article  Google Scholar 

  • Kang SJ (2016) Multi-user identification-based eye-tracking algorithm using position estimation. Sensors 17:41

    Article  Google Scholar 

  • Klare BF, Klein B, Taborsky E, Blanton A, Cheney J, Allen K, Grother P, Mah A, Jain AK (2015) Pushingthe frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, pp: 1931–1939.

  • Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: A convolutional neural-network approach. IEEE Trans Neural Netw 8(1):98–113

    Article  Google Scholar 

  • Li J, Qiu T, Wen C, Xie K, Wen F-Q (2018) Robust face recognition using the deep C2D-CNN model based on decision-level fusion. Sensors 18:2080

    Article  Google Scholar 

  • Li X, Hao P, He L et al (2019) Image gradient orientations embedded structural error coding for face recognition with occlusion. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01257-7

    Article  Google Scholar 

  • Liao S, Chung AC (2007) Face recognition by using elongated local binary patterns with average maximum distance gradient magnitude. In: ACCV: Proceedings of the 8th Asian conference on Computer vision, pp: 672–679.

  • Lyons M, Akamatsu S, Kamachi M, Gyoba J (1998) Coding facial expressions with gabor wavelets. In: Proceedings FGR98 pp: 200–205.

  • Marcel S, Rodriguez Y, Heusch G (2007) On the recent use of local binary patterns for face authentication. In: International Journal on Image and Video Processing Special Issue on Facial Image Processing, pp. 1–9.

  • Mejdoub M, Amar CB (2013) Classification improvement of local feature vectors over the KNN algorithm. Multimed Tools Appl 64:197–218

    Article  Google Scholar 

  • Mu Y, Yan S, Liu Y, Huang T, Zhou B (2008) Discriminative local binary patterns for human detection in personal album. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp: 1–8.

  • Ojala T, Pietikainen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recogn 29(1):51–59.

    Article  Google Scholar 

  • Ojala T, Pietikäinen M, Mäenpää T (2000) Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns. In: Computer Vision-ECCV 2000: ECCV 2000: Lecture Notes in Computer Science. Springer, Berlin.

  • Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with Local Binary Patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  • Patil SA, Deore PJ (2013) Face recognition: a survey. Inform Eng Int J 1(1):31–41

    Google Scholar 

  • Peng C, Bu W, Xiao J, Wong K-C, Yang M (2018) An Improved neural network cascade for face detection in large scene surveillance. Appl Sci 8:2222

    Article  Google Scholar 

  • Rama G, Chouhan VS (2017) Classifiers in image processing. Int J Future Revol Comput Sci Commun Eng 3(6):22–24

    Google Scholar 

  • Rama LR, Babu GR, Kishore L (2012) Face recognition based on eigen features of multi scaled face components and artificial neural network. Int J Secur Appl 5(3):23–44

    Google Scholar 

  • Reddy PAK, Ramaiah GNK, Prasad MNG (2019) Efficient Facial Recognition System using Lbp Technique. Int J Recent Technol Eng 8(1):2277–3878

    Google Scholar 

  • Sanderson C, Paliwal KK (2003) Fast features for face authentication under illumination direction changes. Pattern Recogn Lett 24(14):2409–2419

    Article  Google Scholar 

  • Spacek DL (2012) Face recognition data, University of Essex. UK. Computer Vision Science Research Projects. https://cswww.essex.ac.uk/mv/allfaces/faces94.html. Accessed 22 Jan 2019

  • Tan X, Triggs B (2007) Fusing gabor and LBP feature sets for kernel-based face recognition. In: Proceedings of the Third International Conference on Analysis and Modeling of Faces and Gestures

  • Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. Trans Img Proc 19(6):1635–1650

    Article  MathSciNet  Google Scholar 

  • Wali A, Aoun NB, Karray H, Amar CB, Alimi AM (2010) A new system for event detection from video surveillance sequences. In: Advanced Concepts for Intelligent Vision Systems, Proceedings of the12th International Conference, ACIVS 2010, Sydney, Australia

  • Wiskott L, Krüger N, Kuiger N, Von Der Malsburg C (1997) Face recognition by elastic bunch graph matching. IEEE Trans Pattern Anal Mach Intell 19(7):775–779

    Article  Google Scholar 

  • Xiang S, Nie F, Zhang C (2008) Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recog 41(12):3600–3612

    Article  Google Scholar 

  • Xu W, Lee E (2012) Human face recognition based on improved D-LDA and integrated BPNNs algorithms. Int J Secur Appl 6(2):121–126

    Google Scholar 

  • Xue G et al (2011) Hybrid center-symmetric local pattern for dynamic background subtraction. In: 2011 IEEE International Conference on Multimedia and Expo, pp 1–6

  • Zhang L et al (2007) Face detection based on multi-block lbp representation. Advances in biometrics. Springer, Berlin, pp 11–18

    Google Scholar 

  • Zhao X, Delleandrea E, Chen L (200) A people counting system based on face detection and tracking in a video. In: Proceedings of the Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, Genova, Italy, pp: 67–72

Download references

Acknowledgements

This work is carried out in R & D Center, Kuppam Engineering College, kuppam. I express my gratitude for the people who made valuable contributions directly or indirectly in carrying out this work. I would like to express my gratitude to Dr. S. Nanda Kishor for valuable suggestions in writing the article and the experimental results of my manuscript.

Author information

Authors and Affiliations

  1. JNTU, Ananthapuramu/KEC, Kuppam, India

    P. Ajay Kumar Reddy

  2. Department of ECE, KEC, Kuppam, India

    G. N. Kodanda Ramaiah

  3. Department of ECE, JNTU, Ananthapuramu, India

    M. N. Giriprasad

Authors
  1. P. Ajay Kumar Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. N. Kodanda Ramaiah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. N. Giriprasad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Ajay Kumar Reddy.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04127-x

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Reddy, P.A.K., Ramaiah, G.N.K. & Giriprasad, M.N. RETRACTED ARTICLE: An efficient human face verification system based on ELBP: a high precision feature. J Ambient Intell Human Comput 12, 5127–5136 (2021). https://doi.org/10.1007/s12652-020-01965-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-01965-5

Keywords

Search

Navigation