Skip to main content

Advertisement

SpringerLink
Log in

Interest points reduction using evolutionary algorithms and CBIR for face recognition

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Face recognition has become a fundamental biometric tool that ensures identification of people. Besides a high computational cost, it constitutes an open problem for identifying faces under ideal conditions as well as those under general conditions. Though the advent of high memory and inexpensive computer technologies has made the implementation of face recognition possible in several devices and authentication systems, achieving \(100\%\) face recognition in real time is still a challenging task. This paper implements an evolutionary computer genetic algorithm for optimizing the number of interest points on faces, intended to get a quick and precise facial recognition using local analysis texture technique applied to CBIR methodology. Our approach was evaluated using different databases, getting an efficient facial recognition of up to \(100\%\) considering only seven interest points from a total of 54 cited in the literature. The interest points reduction was possible through a parallel implementation of our approach using a 54-processor cluster that executes the similar task up to \(300\%\) more faster.

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

Similar content being viewed by others

Notes

  1. After Moore’s law | Technology Quarterly |The Economist http://www.economist.com/node/21693710.

  2. What machines can tell from your face. The Economist Print Ed, Sep 9, 2017. The Economist Group Limited.

References

  1. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell 19(7), 711–720 (1997). https://doi.org/10.1109/34.598228

    Article  Google Scholar 

  2. Ben Fredj, H., Bouguezzi, S., Souani, C.: Face recognition in unconstrained environment with cnn. The Vis. Comput. (2020). https://doi.org/10.1007/s00371-020-01794-9

  3. Benavides, C., Villegas, J., Román, G., C., A.: Face recognition using cbir techniques. In: X Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados, pp. 733–740. Universidad de Extremadura (2015)

  4. Benavides-Alvarez, C., Villegas-Cortez, J., Román-Alonso, G., Aviles-Cruz, C.: Face classification by local texture analisys through cbir and surf points. Rev. IEEE Am. Latina 14(5), 2418–2424 (2016)

    Article  Google Scholar 

  5. Chaudhry, S., Chandra, R.: Face detection and recognition in an unconstrained environment for mobile visual assistive system. Appl. Soft Comput. 53, 168–180 (2017). https://doi.org/10.1016/j.asoc.2016.12.035

    Article  Google Scholar 

  6. Chávez, F., Fernández, F., Benavides, C., Lanza, D., Villegas, J., Trujillo, L., Olague, G., Román, G.: ECJ+HADOOP: An Easy Way to Deploy Massive Runs of Evolutionary Algorithms, pp. 91–106. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-31153-1_7

    Book  Google Scholar 

  7. Chávez, F., Fernández de Vega, F., Lanza, D., Benavides, C., Villegas, J.,Trujillo, L., Olague, G., Román, G.: Deploying massive runs of evolutionary algorithms with ECJ and hadoop: Reducing interest points required for face recognition. Int. J. High Perform. Comput. Appl. 32(5), 706–720 (2018). https://doi.org/10.1177/1094342016678302

  8. Clemente, E., Chavez, F., de Vega, F.F., Olague, G.: Self-adjusting focus of attention in combination with a genetic fuzzy system for improving a laser environment control device system. Appl. Soft Comput. 32, 250–265 (2015). https://doi.org/10.1016/j.asoc.2015.03.011

    Article  Google Scholar 

  9. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Natural Computing Series. Springer, Berlin (2003). https://doi.org/10.1007/978-3-662-05094-1

    Book  MATH  Google Scholar 

  10. Ekenel, H., Stiefelhagen, R.: Why is facial occlusion a challenging problem? In: Tistarelli, M., Nixon, M. (eds.) Advances in Biometrics. Lecture Notes in Computer Science, vol. 5558, pp. 299–308. Springer, Berlin Heidelberg (2009)

  11. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001). https://doi.org/10.1109/34.927464

    Article  Google Scholar 

  12. Gupta, S., Thakur, K., Kumar, M.: 2d-human face recognition using sift and surf descriptors of face’s feature regions. Vis. Comput. (2020). https://doi.org/10.1007/s00371-020-01814-8

  13. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. University of Michigan Press, USA (1992)

    Book  Google Scholar 

  14. Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the hausdorff distance. In: Bigun, J., Smeraldi, F. (eds.) Audio- and Video-Based Biometric Person Authentication, pp. 90–95. Springer, Berlin (2001)

    Chapter  Google Scholar 

  15. Krisshna, N.A., Deepak, V.K., Manikantan, K., Ramachandran, S.: Face recognition using transform domain feature extraction and pso-based feature selection. Appl. Soft Comput. 22, 141–161 (2014). https://doi.org/10.1016/j.asoc.2014.05.007

    Article  Google Scholar 

  16. Li, H., Zhou, D., Nie, R.: Analysis of Face Recognition Methods in Linear Subspace, pp. 3045–3051. Springer, Dordrecht (2014)

    Google Scholar 

  17. Liu, C., Chen, K., Xu, Y.: Study of face recognition technology based on STASM and its application in video retrieval. In: Fei, M., Peng, C., Su, Z., Song, Y., Han, Q. (eds.) Computational Intelligence, Networked Systems and Their Applications. ICSEE 2014, LSMS 2014. Communications in Computer and Information Science, vol 462. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45261-5_23

  18. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004). https://doi.org/10.1023/B:VISI.0000029664.99615.94

    Article  Google Scholar 

  19. Martinez, A., Benavente., R.: The ar face database. CVC Technical Report 24, The Ohio State University (1998)

  20. Martinez, A.M.: Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 748–763 (2002). https://doi.org/10.1109/TPAMI.2002.1008382

    Article  Google Scholar 

  21. Milborrow, S., Morkel, J., Nicolls, F.: The MUCT Landmarked Face Database. Pattern Recognition Association of South Africa (2010). http://www.milbo.org/muct

  22. Milborrow, S., Nicolls, F.: Active shape models with sift descriptors and mars. In: 2014 International Conference on Computer Vision Theory and Applications (VISAPP), vol. 2, pp. 380–387 (2014)

  23. Olague, G., Trujillo, L.: Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming. Image Vis. Comput. 29(7), 484–498 (2011). https://doi.org/10.1016/j.imavis.2011.03.004

    Article  Google Scholar 

  24. Perez, C.B., Olague, G.: Genetic programming as strategy for learning image descriptor operators. Intell. Data Anal. 17(4), 561–583 (2013). https://doi.org/10.3233/IDA-130594

    Article  Google Scholar 

  25. Raghuwanshi, G., Mishra, N., Sharma, S.: Content Based Image Retrieval Using Implicit And Explicit Feedback With Interactive Genetic Algorithm. Int. J. Comput. Appl. 43, 8–14 (2012). https://doi.org/10.5120/6186-8665

    Article  Google Scholar 

  26. Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), CVPR ’12, pp. 2879–2886. IEEE Computer Society, Washington, DC, USA (2012). http://dl.acm.org/citation.cfm?id=2354409.2355119

  27. Sengupta, S., Chen, J., Castillo, C., Patel, V.M., Chellappa, R., Jacobs, D.W.: Frontal to profile face verification in the wild. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–9 (2016)

  28. Serrano-Talamantes, J.F., Aviles-Cruz, C., Villegas-Cortez, J., Sossa-Azuela, J.H.: Self organizing natural scene image retrieval. Expert Syst. Appl. 40(7), 2398–2409 (2012). https://doi.org/10.1016/j.eswa.2012.10.064

    Article  Google Scholar 

  29. Srinivasan, A., Balamurugan, V.: A Novel Approach for Facial Feature Extraction in Face Recognition, pp. 155–162. Springer, Cham (2014)

    Google Scholar 

  30. Sumana, I.J., Islam, M.M., Zhang, D., Lu, G.: Content based image retrieval using curvelet transform. In: Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing, MMSP 2008 (2008). https://doi.org/10.1109/MMSP.2008.4665041

  31. Torres, RdS, Falcão, A.X., Gonçalves, M.A., Papa, J.P., Zhang, B., Fan, W., Fox, E.A.: A genetic programming framework for content-based image retrieval. Pattern Recognition 42, 283–292 (2009). https://doi.org/10.1016/j.patcog.2008.04.010

    Article  MATH  Google Scholar 

  32. Trujillo, L., Olague, G.: Automated design of image operators that detect interest points. Evolutionary Computation. 16(4), 483–507 (2008)

    Article  Google Scholar 

  33. Ugail, H., Al-dahoud, A.: Is gender encoded in the smile? a computational framework for the analysis of the smile driven dynamic face for gender recognition. Vis. Comput. 34(9), 1243–1254 (2018). https://doi.org/10.1007/s00371-018-1494-x

    Article  Google Scholar 

  34. Ramakrishnan, S.: Face recognition - semisupervised classification, subspace projection and evaluation methods. ISBN: 978-953-51-2422-1 (2016). https://doi.org/10.5772/61471

  35. Wang, M., Deng, W.: Deep face recognition: A survey. arXiv:1804.06655 (2018)

  36. Zhao, T., Lu, J., Zhang, Y., Xiao, Q.: Feature selection based-on genetic algorithm for CBIR. In: Proceedings - 1st International Congress on Image and Signal Processing, CISP 2008 (2008). https://doi.org/10.1109/CISP.2008.90

Download references

Acknowledgements

This work has been supported by Fundación Carolina, Spain, under the scholarship program 2016–2017. This work has been developed under Grant “Evolución de descriptores estadísticos de textura de superficie para implementación en clasificación de imágenes digitales,” UAM-CBI EL006-18. The authors would like to thank Spanish Ministry of Economy, Industry and Competitiveness and European Regional Development Fund (FEDER) under Projects TIN2014-56494- C4-4-P (Ephemec) and TIN2017-85727-C4-4-P (DeepBio); Junta de Extremadura Project IB16035 Regional Government of Extremadura, Consejeria of Economy and Infrastructure, FEDER. Cesar Benavides thanks the CONACyT for the scholarship support.

Author information

Authors and Affiliations

  1. Departamento de Sistemas, Universidad Autónoma Metropolitana, Azcapotzalco, Av. San Pablo Xalpa No.180, Col Reynosa Tamaulipas, CP 02200, Ciudad de México, México

    Juan Villegas-Cortez

  2. Departamento de Electrónica, Universidad Autónoma Metropolitana, Azcapotzalco, Av. San Pablo Xalpa No.180, Col Reynosa Tamaulipas, CP 02200, Ciudad de México, México

    César Benavides-Alvarez & Carlos Avilés-Cruz

  3. Departamento de Ing. Eléctrica, Universidad Autónoma Metropolitana, Iztapalapa, San Rafael Atlixco 186, Vicentina, CP 09340, Ciudad de México, México

    Graciela Román-Alonso

  4. Department of Computer Science, University of Extremadura, C/. Santa Teresa de Jornet, 38, CP: 06800, Mérida, Spain

    Francisco Fernández de Vega & Francisco Chávez

  5. Departamento de Química, Universidad Autónoma Metropolitana, Iztapalapa, San Rafael Atlixco 186, Vicentina, CP 09340, Ciudad de México, México

    Salomón Cordero-Sánchez

Authors
  1. Juan Villegas-Cortez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. César Benavides-Alvarez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carlos Avilés-Cruz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Graciela Román-Alonso
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Francisco Fernández de Vega
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Francisco Chávez
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Salomón Cordero-Sánchez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan Villegas-Cortez.

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

Villegas-Cortez, J., Benavides-Alvarez, C., Avilés-Cruz, C. et al. Interest points reduction using evolutionary algorithms and CBIR for face recognition. Vis Comput 37, 1883–1897 (2021). https://doi.org/10.1007/s00371-020-01949-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-020-01949-8

Keywords

Search

Navigation