Skip to main content
SpringerLink
Log in

Addressing facial dynamics using k-medoids cohort selection algorithm for face recognition

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Face recognition is itself a very challenging task and it becomes more challenging when the input images have intra class variations and inter class similarities in a large scale. Yet the recognition accuracy can be improved in some extent by supporting the system with non-matched templates. Therefore a set of cohort images is used in this regard. But all the cohort templates of the initial cohort pool may not be relevant for each and every enrolled subject. So the main focus of this work is to select a subject specific and meaningful cohort subset. This paper proposes a cohort selection method called K-medoids Cohort Selection (KMCS) to select a reference set of non-matched templates which are almost appropriate to the respective subjects. Basically, all cohort scores of a subject are clustered first using K-medoids clustering. Afterward the cluster having more scattered members/scores from its medoid is selected as a cohort subset because this cluster is constituted with the cohorts carrying more discriminative features compared to others. The SIFT points and SURF points are extracted as facial feature. The experiments are conducted on FEI, ORL and Look-alike databases of face images. The matching scores between probe and query images are normalized using T-norm, Max-Min and Aggarwal (Max rule) cohort score normalization techniques before taking the final decision of acceptance or rejection. The results obtained from the experiments show the domination of the proposed system over the non-cohort face recognition system as well as random and Top 10 cohort selection methods. There is another comparative study between k-means and K-medoids clustering for cohort selection.

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24

Similar content being viewed by others

References

  1. Aggarwal G, Ratha NK, Bolle RM (2006) Biometric verification: Looking beyond raw similarity scores. In Proceedings of Computer Vision and Pattern Recognition Workshop, 31-31

  2. Aggarwal G, Ratha NK, Bolle RM, Chellappa R (2008) Multi-biometric cohort analysis for biometric fusion. In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 5224-5227). IEEE

  3. Ahmad W, Karnick H, Hegde RM (2018) Client-wise cohort set selection by combining speaker-and phoneme-specific I-vectors for speaker verification. Multimed Tools Appl 77(7):8273–8294

    Article  Google Scholar 

  4. Auckenthaler R, Carey M, Thomas HL (2000) Score normalization for text-independent speaker verification systems. Digital Signal Processing 10:42–54

    Article  Google Scholar 

  5. Bay H, Tuytelaars T, Van Gool L (2006) Surf: Speeded up robust features. In European conference on computer vision (pp. 404-417). Springer Berlin Heidelberg

  6. Bezdek JC, Ehrlich R, Full W (1984) FCM: The fuzzy c-means clustering algorithm. Comput Geosci 10(2-3):191–203

    Article  Google Scholar 

  7. Dahmouni A, El Moutaouakil K, Satori K (2018). Face description using electric virtual binary pattern (EVBP): application to face recognition. Multimedia Tools and Applications 1-19

  8. Deng W, Hu J, Guo J (2012) Extended SRC: Undersampled face recognition via intraclass variant dictionary. IEEE Trans Pattern Anal Mach Intell 34(9):1864–1870

    Article  Google Scholar 

  9. Eickeler S, Muller S, Rigoll G (1999) High performance face recognition using pseudo 2-d hidden markov models. In: Control Conference (ECC), 1999 European, pp. 3023-3028. IEEE

  10. Frost WH (1939) The age selection of mortality from tuberculosis in successive decades. Am J Hyg 30:91–95

    Google Scholar 

  11. Garain J, Kumar RK, Kisku DR Sanyal G (2016). Selection of user-dependent cohorts using bezier curve for person identification. In: International Conference Image Analysis and Recognition (pp. 566-572). Springer International Publishing

  12. Garain J, Kumar RK, Kumar D, Kisku DR, Sanyal G, (2018) A Bezier curve cohort selection strategy for face pair matching. Second International Conference on Digital Signal Processing, Tokyo, Japan, ACM ICPS, February 25-27, In press

  13. Garain J, Kumar RK, Kumar D, Kisku DR, Sanyal G (2018) Image Specific Cross Cohort Normalization for Face Pair matching. International Conference on Computational Intelligence and Data Science (ICCIDS), Gurugram, India, Procedia Elsevier, (In Press)

  14. Garain J, Kumar RK, Sanyal G, Kisku DR (2015) Cohort selection of specific user using Max-Min-Centroid-Cluster (MMCC) method to enhance the performance of a biometric system. International Journal of Security and Its Applications 9(6):263–270

    Article  Google Scholar 

  15. Garain J, Shah A, Kumar RK, Sanyal G, Kisku DR (2016) BCP-BCS: best-fit cascaded matching paradigm with cohort selection using bezier curve for individual recognition. In: Asian Conference on Computer Vision, pp. 377-390. Springer, Cham

  16. Ghinea G, Kannan R, Kannaiyan S (2014) Gradient-orientation-based PCA subspace for novel face recognition. IEEE Access 2:914–920

    Article  Google Scholar 

  17. Ghosh S, Dubey SK (2013) Comparative analysis of k-means and fuzzy c-means algorithms. Int J Adv Comput Sci Appl 4:4

    Google Scholar 

  18. Gomathi E, Baskaran K (2010) Recognition of faces using improved principal component analysis. In: Machine Learning and Computing (ICMLC), 2010 Second International Conference on, pp. 198-201. IEEE

  19. Kramer RS, Young AW, Burton AM (2018) Understanding face familiarity. Cognition 172:46–58

    Article  Google Scholar 

  20. Kumar D, Garain J, Kisku DR, Sing JK, Gupta P (2018) Ensemble face recognition system using dense local graph structure. In: International Conference on Intelligent Computing, pp. 846-852. Springer, Cham

  21. Lam K-M, Yan H (1998) An analytic-to-holistic approach for face recognition based on a single frontal view. IEEE Transactions on Pattern Analysis & Machine Intelligence 7:673–686

    Google Scholar 

  22. Lamba H, Sarkar A, Vatsa M, Singh R Noore A (2011) Face recognition for look-alikes: A preliminary study. In Biometrics (IJCB), International Joint Conference on (pp. 1-6). IEEE

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

    Article  Google Scholar 

  24. Li H, Hua G (2018) Probabilistic elastic part model: a pose-invariant representation for real-world face verification. IEEE Trans Pattern Anal Mach Intell 40(4):918–930

    Article  Google Scholar 

  25. Li SZ, Juwei L (1999) Face recognition using the nearest feature line method. IEEE Trans Neural Netw 10(2):439–443

    Article  Google Scholar 

  26. Liu J, Pengren A, Ge Q, Zhao H (2018) Gabor tensor based face recognition using the boosted nonparametric maximum margin criterion. Multimed Tools Appl 77(7):9055–9069

    Article  Google Scholar 

  27. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  28. Lucas SM (1997) Face recognition with the continuous n-tuple classi¢ er. In: Proceedings of the British Machine Vision Conference

  29. Merati A, Poh N, Kittler J (2010) Extracting discriminative information from cohort models. In: Proceedings of 4th IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), 1-6

  30. Merati A, Poh N, Kittler J (2012) User-specific cohort selection and score normalization for biometric systems. IEEE Transactions on Information Forensics and Security 7(4)

  31. Nefian AV, Hayes MH (1998) Hidden Markov models for face recognition. In Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on, vol. 5, pp. 2721-2724. IEEE

  32. Rakshit RD, Nath SC, Kisku DR (2018) Face Identification using Some Novel Local Descriptors under the Influence of Facial Complexities. Expert Systems with Applications - An International Journal 92(2):82–94 Elsevier

    Article  Google Scholar 

  33. Rosenberg AE, DeLong J, Lee C-H, Juang B-H, Soong FK (1992) The use of cohort normalized scores for speaker verification. In: Second international conference on spoken language processing

  34. Samaria FS, Harter AC (1994). Parameterisation of a stochastic model for human face identification. In Applications of Computer Vision, Proceedings of the Second IEEE Workshop on (pp. 138-142). IEEE

  35. Schroff F, Treibitz T, Kriegman D, Belongie S (2011) Pose, illumination and expression invariant pairwise face-similarity measure via doppelgänger list comparison. In 2011 International Conference on Computer Vision (pp. 2494-2501). IEEE

  36. Semwal VB, Mondal K, Nandi GC (2017) Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Comput & Applic 28(3):565–574

    Article  Google Scholar 

  37. Semwal VB, Raj M, Nandi GC (2015) Biometric gait identification based on a multilayer perceptron. Robot Auton Syst 65:65–75

    Article  Google Scholar 

  38. Semwal VB, Singha J, Sharma PK, Chauhan A, Behera B (2017) An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification. Multimed Tools Appl 76(22):24457–24475

    Article  Google Scholar 

  39. Sir RD (2001) Cohort studies: History of the method I. prospective cohort studies. International Journal of Public Health 46(2):75

    Article  MathSciNet  Google Scholar 

  40. Sir RD (2001) Cohort studies: History of the method II. Retrospective cohort studies. International Journal of Public Health 46(3):152

    Article  Google Scholar 

  41. Soldera J, Behaine CAR, Scharcanski J (2015) Customized orthogonal locality preserving projections with soft-margin maximization for face recognition. IEEE Trans Instrum Meas 64(9):2417–2426

    Article  Google Scholar 

  42. Sun Y, Chen Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification. In: Advances in neural information processing systems (pp. 1988-1996)

  43. Sun Y, Tistarelli M Poh N (2013) Picture-specific cohort score normalization for face pair matching. In Biometrics: Theory, Applications and Systems (BTAS), 2013 IEEE Sixth International Conference on (pp. 1-8). IEEE

  44. Taigman Y, Yang M, Ranzato MA, Wolf L (2014) Deepface: Closing the gap to human-level performance in face verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1701-1708)

  45. Thomaz CE, Giraldi GA (2010) A new ranking method for Principal Components Analysis and its application to face image analysis. Image Vis Comput 28(6):902–913

    Article  Google Scholar 

  46. Tistarelli M, Sun Y, Poh N (2014) On the use of discriminative cohort score normalization for unconstrained face recognition. IEEE Transactions on Information Forensics and Security 9(12):2063–2075

    Article  Google Scholar 

  47. Tulyakov S, Zhang Z, Govindaraju V (2008) Comparison of combination methods utilizing T-normalization and second best score model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshop, 1-5

  48. Wagner A, Wright J, Ganesh A, Zhou Z Ma Y (2009) Towards a practical face recognition system: Robust registra tion and illumination by sparse representation. In: IEEE Com puter Society Conference on Computer Vision and Pattern Recognition (Vol. 2, p. 3)

  49. Wang W, Yang J, Xiao J, Li S, Zhou D (2015) Face Recognition Based on Deep Learning. In: Zu Q, Hu B, Gu N, Seng S (eds) Human Centered Computing. HCC 2014. Lecture Notes in Computer Science, vol 8944. Springer, Cham

    Google Scholar 

  50. Wolf L, Hassner T, Taigman Y (2009) Similarity scores based on background samples. In Asian Conference on Computer Vision (pp. 88-97). Springer Berlin Heidelberg

  51. Wolf L, Hassner T, Taigman Y (2009) The one-shot similarity kernel. In 2009 IEEE 12th International Conference on Computer Vision (pp. 897-902). IEEE

  52. Yang X, Liu F, Tian L, Li H, Jiang X (2018) Pseudo-full-space representation based classification for robust face recognition. Signal Process Image Commun 60:64–78

    Article  Google Scholar 

  53. Yang M, Van Gool L, Zhang L (2013) Sparse variation dictionary learning for face recognition with a single training sample per person. In Proceedings of the IEEE international conference on computer vision (pp. 689-696)

  54. Yin Q, Tang X, Sun J (2011) An associate-predict model for face recognition. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on (pp. 497-504). IEEE

  55. Zadeh LA (1988) Fuzzy logic. Computer 21(4):83–93

    Article  Google Scholar 

Download references

Acknowledgements

This work is funded by Digital India Corporation (formerly Media Lab Asia), Deity, Govt. of India.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Durgapur, Durgapur, West Bengal, 713209, India

    Jogendra Garain, Ravi Kant Kumar, Dakshina Ranjan Kisku & Goutam Sanyal

Authors
  1. Jogendra Garain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ravi Kant Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dakshina Ranjan Kisku
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Goutam Sanyal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jogendra Garain.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Garain, J., Kumar, R.K., Kisku, D.R. et al. Addressing facial dynamics using k-medoids cohort selection algorithm for face recognition. Multimed Tools Appl 78, 18443–18474 (2019). https://doi.org/10.1007/s11042-018-7132-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-7132-9

Keywords

Search

Navigation