Skip to main content
SpringerLink
Log in

Machine learning approach for face image retrieval

  • ICONIP2010
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Face image retrieval (FIR) is useful to many domain applications, such as helping police to catch criminals or managing householders. However, little research has been done that uses individual face features for image comparison and retrieval. This paper aims to develop a machine learning approach for face image retrieval based on the local face features of the eyes, nose, and mouth. Neural networks are used to localise facial features, and to implement a learning pseudo metric (LPM) to filter out irrelevant images for retrieval efficiently based on semantic information. Our FIR system performs below average given traditional performance measures, but inspecting actual retrieved images it shows strong promise. It is observed that the LPM semantic filtering method was found to reduce the database size by up to 50% without a significant reduction in retrieval performance.

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

Notes

  1. Any distance measure can be used in this stage.

  2. Known as stratified k-fold cross-validation.

  3. The 13 images consisted of neutral expression, smiling, angry, screaming, neural expression with right light, neural expression with left light, neural expression with both lights on, sunglasses, sunglasses with right light on, sunglasses with left light on, scarf, scarf with right light on, and scarf with left light on.

References

  1. Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40:1–60

    Article  Google Scholar 

  2. Fauzi MFA, Lewis PH (2009) Query by low-quality image. Image Vis Comput 27:713–724

    Article  Google Scholar 

  3. Liu W, Tang X, Liu J (2007) Bayesian tensor inference for sketch-based facial photo hallucination. In: Veloso MM (ed) Proceeding international joint conference of artifical intelligence. Morgan Kaufmann Publishers Inc., San Francisco, pp 2141–2146

  4. Sridharan K, Nayak S, Chikkerur S, Govindaraju V (2005) A probabilistic approach to semantic face retrieval system. In: Kanade T, Jain A, Ratha NK (eds) Audio- and video-based biometric person authentication, vol 3546 of lecture notes in computer science. Springer, Berlin, pp 977–986

  5. Ai H, Liang L, Xiao X, Xu G (2001) Face indexing and retrieval in personal digital album. In: Proceedings of 2nd IEEE Pacific Rim conference multimedia, vol 2195, Springer-Verlag, London, UK, pp 48–54

  6. Gao Y, Qi Y (2005) Robust visual similarity retrieval in single model face databases. Pattern Recognit 38:1009–1020

    Article  Google Scholar 

  7. Wu B, Ai H, Huang C (2004) Facial image retrieval based on demographic classification. In: Proceedings of 17th International Conference of Pattern Recognition, vol 3. IEEE Computer Society, Washington, pp 914–917

  8. Santini S, Jain R (1999) Similarity measures. IEEE Trans Pattern Anal Mach Intell 21:871–883

    Article  Google Scholar 

  9. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal Mach sIntell 22:1349–1380

    Article  Google Scholar 

  10. Liu Y, Zhang D, Lu G, Ma W-Y (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recognit 40:262–282

    Article  MATH  Google Scholar 

  11. Wang DH, Ma XH, Kim YS (2005) Learning pseudo metric for intelligent multimedia data classification and retrieval. J Intell Manuf 16:575–586

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Cai D, He X, Han J, Zhang H-J (2006) Orthogonal laplacianfaces for face recognition. IEEE Trans Image Process 15:3608–3614

    Article  Google Scholar 

  15. He X, Cai D, Han J (2008) Learning a maximum margin subspace for image retrieval. IEEE Trans Knowl Data Eng 20:189–201

    Article  Google Scholar 

  16. Yang M-H, Kriegman DJ, Ahuja N (2002) Detecting faces in images: a survey. IEEE Trans Pattern Anal Mach Intell 24:34–58

    Article  Google Scholar 

  17. Zhou H, Yuan Y, Sadka AH (2008) Application of semantic features in face recognition. Pattern Recogn 41:3251–3256

    Article  MATH  Google Scholar 

  18. Conilione P, Wang DH (2011) Automatic localization and annotation of facial features using machine learning techniques. Soft Comput 15:1231–1245

    Google Scholar 

  19. Wang DH, Ma XH (2005) A hybrid image retrieval system with user’s relevance feedback using neurocomputing. Informatica 29:271–279

    Google Scholar 

  20. Lu Y, Guo H, Feldkamp L, Robust neural learning from unbalanced data samples. In: Proceedings of IEEE International Joint Conferences of Neural Networks, vol 3. Anchorage, Alaska, USA, pp 1816–1821

  21. Wang DH, Kim Y-S, Park SC, Lee CS, Han YK (2007) Learning based neural similarity metrics for multimedia data mining. Soft Comput 11:335–340

    Article  Google Scholar 

  22. Martinez AM, Benavente R (1998) The AR face database, technical report 24, Computer Visual Center

  23. Mller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, VIC, 3086, Australia

    Dianhui H. Wang & Paul Conilione

Authors
  1. Dianhui H. Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul Conilione
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dianhui H. Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, D.H., Conilione, P. Machine learning approach for face image retrieval. Neural Comput & Applic 21, 683–694 (2012). https://doi.org/10.1007/s00521-011-0665-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-011-0665-8

Keywords

Search

Navigation