Skip to main content
SpringerLink
Log in

An Evaluation of Local Binary Descriptors for Facial Emotion Classification

  • Chapter
  • First Online:
Book cover Innovations in Computer Science and Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 103))

Abstract

Feature descriptors are vitally important in the broad domain of computer vision. In software systems for face recognition, local binary descriptors find wide use as feature descriptors. Because they give more robust results in varying conditions such as pose, lighting and illumination changes. Precision depends on the correctness of representing the relationship in the local neighbourhood of a digital image into small structures. This paper presents the performance analysis of various binary descriptors such as local binary pattern (LBP), local directional pattern (LDP), local directional number pattern (LDNP), angular local directional pattern (ALDP), local optimal-oriented pattern (LOOP), support vector machine (SVM), K-nearest neighbour (KNN) and back propagation neural network (BPNN) are used for emotion classification. The results indicate that ALDP + Polynomial SVM on MUFE, JAFFE and Yale Face databases gives better accuracy with 96.00%, 94.44% and 89.00%, respectively.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rashid TA (2016) Convolutional neural networks based method for improving facial expression recognition. Intell Syst Technol Appl 530

    Google Scholar 

  2. Ekman P, Friesen WV (1971) Constants across cultures in the face and emotion. J Pers Soc Psychol 17(2):124–129

    Article  Google Scholar 

  3. Kumari J, Rajesh R, Pooja KM (2015) Facial expression recognition: a survey. Procedia Comput Sci 58:486–491

    Article  Google Scholar 

  4. Ding C, Xu C, Tao D (2015) Multi-task pose-invariant face recognition. IEEE Trans Image Process 24(3):980–993

    Google Scholar 

  5. Kan M, Shan S, Zhang H, Lao S, Chen X (2016) Multiview discriminant analysis. IEEE Trans Pattern Anal Mach Intell 38(1):188–194

    Article  Google Scholar 

  6. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  7. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Article  Google Scholar 

  8. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces versus fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  9. Ahonen T, Hadid A, Pietikainen M (2006) Face recognition with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041

    Article  Google Scholar 

  10. Rivera AR, Castillo JR, Chae O (2013) Local directional number pattern for face analysis: face and recognition. IEEE Trans Image Process 22(5):1740–1752

    Google Scholar 

  11. Su Y, Shan S, Chen X, Gao W (2009) Hierarchical ensemble of global and local classifiers for face recognition. IEEE Trans Image Process 18(8):1885–1896

    Article  MathSciNet  Google Scholar 

  12. Wolf L, Hassner T, Taigman Y (2011) Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE Trans Pattern Anal Mach Intell 33(10):1978–1990

    Article  Google Scholar 

  13. Huang D, Shen C, Ardabilian M, Wang Y, Chen L (2011) Local binary patterns and its application to facial image analysis: a survey. IEEE Trans Syst Man and Cyber-Part C 41(6):765–781

    Article  Google Scholar 

  14. Ojala T, Pietikinen M, Harwood D (1994) Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In Proc, ICPR

    Book  Google Scholar 

  15. Jabid T, Kabir MH, Chae O (2010) Local directional pattern (LDP) for face recognition. Proc Int Conf IEEE on Consum Electr 329–330

    Google Scholar 

  16. Zhong F, Zhang J (2013) Face recognition with enhanced local directional patterns. Neurocomputing 119:375–384

    Article  Google Scholar 

  17. Jabid T, Kabir MH, Chae O (2010) Robust facial expression recognition based on local directional pattern. ETRI J 32(5):784–794

    Article  Google Scholar 

  18. Shabat AM, Tapamo JR (2014) A comparative study of local directional pattern for texture classification. In: Proc Int Conf IEEE World Symp Computer Applications & Research (WSCAR), pp 1–7

    Google Scholar 

  19. Shabat AMM, Tapamo J (2018) Angled local directional pattern for texture analysis with an application to facial expression recognition. IET Comput Vision 12(5):603–608

    Article  Google Scholar 

  20. Chakraborti T, McCane B, Mills S, Pal U (2018) LOOP descriptor: local optimal-oriented pattern. IEEE Signal Process Lett 25(5):635–639

    Article  Google Scholar 

  21. Ramirez Rivera A, Rojas Castillo J, Oksam Chae O (2013) Local directional number pattern for face analysis: face and expression recognition. IEEE Trans Image Process 22(5):1740–1752

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science & IT, Amrita Vishwa Vidyapeetham University, Kochi, India

    R. Arya & E. R. Vimina

Authors
  1. R. Arya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E. R. Vimina
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Guru Nanak Institutions, Hyderabad, Telangana, India

    Harvinder Singh Saini

  2. Guru Nanak Institutions, Hyderabad, Telangana, India

    Rishi Sayal

  3. School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  4. Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Hyderabad, Hyderabad, Telangana, India

    Govardhan Aliseri

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Arya, R., Vimina, E.R. (2020). An Evaluation of Local Binary Descriptors for Facial Emotion Classification. In: Saini, H., Sayal, R., Buyya, R., Aliseri, G. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 103. Springer, Singapore. https://doi.org/10.1007/978-981-15-2043-3_24

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-2043-3_24

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2042-6

  • Online ISBN: 978-981-15-2043-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation