Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Biologically inspired makeup detection system with application in face recognition

© The Institution of Engineering and Technology
Received 15/03/2018, Accepted 26/07/2018, Revised 12/06/2018, Published 03/08/2018

The authors propose a novel-makeup detection approach that assists face recognition systems to achieve a higher-accuracy rate while dealing with makeup images. Makeup features are defined in this work using biologically inspired features (BIFs). To establish makeup depictive features more specifically, colour and texture features are essential to be extracted from images. Hence, they create makeup depictive features in the last complex layer of BIFs (C2) as average skin tone (AST) and a histogram of oriented gradient (HOG), where AST is representative of colour and HOG exhibits texture. The proposed makeup BIFs are extracted from grayscale images and instead of breaking the face image into several patches, the whole face image is employed. This resulted in the performance acceleration as well as higher accuracy rate compared with state-of-the-art makeup detection schemes. Subsequently, they employ a machine learning scheme to train the makeup detection system by feeding it makeup and non-makeup labelled images. They exploit the correlation-based method for the face recognition system and compare the results with the direct two-dimensional principal component analysis face recognition scheme for makeup datasets. Experimental results show the highest accuracy rate of 97.07% was achieved by the proposed algorithm for face recognition system considering makeup.

Inspec keywords: learning (artificial intelligence); face recognition; principal component analysis; image texture; feature extraction

Other keywords: novel-makeup detection approach; makeup BIFs; highest accuracy rate; makeup images; makeup detection system; grayscale images; biologically inspired features; assists face recognition; face image; makeup features; texture features; colour; nonmakeup labelled images; HOG exhibits texture; higher accuracy rate; makeup depictive features; state-of-the-art makeup detection schemes; two-dimensional principal component analysis face recognition scheme; face recognition system; makeup datasets; higher-accuracy rate

Subjects: Computer vision and image processing techniques; Image recognition; Other topics in statistics

References

    1. 1)
      • 19. Serre, T., Riesenhuber, M.: ‘Realistic modeling of simple and complex cell tuning in the HMAX model, and implications for invariant object recognition in cortex’ (Massachusetts Institute of Technology Cambridge Computer Science and Artificial Intelligence Lab, 2004), www.dtic.mil.
    2. 2)
      • 4. Bottino, A., Laurentini, A.: ‘The analysis of facial beauty: an emerging area of research in pattern analysis’. Int. Conf. Image Analysis and Recognition, Póvoa de Varzim, Portugal, 2010, pp. 425–435.
    3. 3)
      • 11. Guo, G., Mu, G., Fu, Y., et al: ‘Human age estimation using bio-inspired features’. IEEE Conf. on Computer Vision and Pattern Recognition. CVPR 2009, Miami, FL, USA, 2009, pp. 112–119.
    4. 4)
      • 24. Gunn, S. R.: ‘Support vector machines for classification and regression’, ISIS Technol. Rep., 1998, 14, (1), pp. 5–16.
    5. 5)
      • 3. Jain, A. K., Klare, B., Park, U.: ‘Face recognition: some challenges in forensics’. 2011 IEEE Int. Conf. on Automatic Face & Gesture Recognition and Workshops (FG 2011), Santa Barbara, California, USA, 2011, pp. 726–733.
    6. 6)
      • 6. Zhao, W., Chellappa, R., Phillips, P. J., et al: ‘Face recognition: a literature survey’, ACM Comput. Surv. CSUR, 2003, 35, (4), pp. 399–458.
    7. 7)
      • 9. Jhuang, H., Serre, T., Wolf, L., et al: ‘A biologically inspired system for action recognition’. Computer Vision, 2007. IEEE 11th Int. Conf. on ICCV 2007, Rio de Janeiro, Brazil, 2007, pp. 1–8.
    8. 8)
      • 14. Wang, X., Kambhamettu, C.: ‘A new approach for face recognition under makeup changes’. 2015 IEEE Global Conf. on Signal and Information Processing (GlobalSIP), Orlando, FL, USA, 2015, pp. 423–427.
    9. 9)
      • 10. Ghodrati, M., Khaligh-Razavi, S.-M., Ebrahimpour, R., et al: ‘How can selection of biologically inspired features improve the performance of a robust object recognition model?’, PLoS ONE, 2012, 7, (2), p. e32357.
    10. 10)
      • 8. Chen, C., Dantcheva, A., Ross, A.: ‘Automatic facial makeup detection with application in face recognition’. 2013 Int. Conf. on Biometrics (ICB), Madrid, Spain, 2013, pp. 1–8.
    11. 11)
      • 13. Rasti, S., Masnadi-Shirazi, M. A., Elahi, G.: ‘Makeup detection using local fisher discriminant analysis’. 2015 7th Conf. on Information and Knowledge Technology (IKT), Urmia, Iran, 2015, pp. 1–6.
    12. 12)
      • 7. Guo, G., Wen, L., Yan, S.: ‘Face authentication with makeup changes’, IEEE Trans. Circuits Syst. Video Technol., 2014, 24, (5), pp. 814–825.
    13. 13)
      • 25. Yang, J., Zhang, D., Frangi, A. F., et al: ‘Two-dimensional PCA: a new approach to appearance-based face representation and recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26, (1), pp. 131–137.
    14. 14)
      • 23. Wold, S., Esbensen, K., Geladi, P.: ‘Principal component analysis’, Chemom. Intell. Lab. Syst., 1987, 2, (1–3), pp. 37–52.
    15. 15)
      • 5. Hayashi, J., Yasumoto, M., Ito, H., et al: ‘Age and gender estimation based on wrinkle texture and color of facial images’. 16th Int. Conf. on Pattern Recognition, 2002. Proc., Quebec City, Quebec, Canada, Canada, 2002, vol. 1, pp. 405–408.
    16. 16)
      • 1. Dantcheva, A., Chen, C., Ross, A.: ‘Can facial cosmetics affect the matching accuracy of face recognition systems?’. 2012 IEEE Fifth Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS), Washington DC, USA, 2012, pp. 391–398.
    17. 17)
      • 22. Dalal, N., Triggs, B.: ‘Histograms of oriented gradients for human detection’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition. CVPR 2005, San Diego, CA, USA, 2005, vol. 1, pp. 886–893.
    18. 18)
      • 15. Riesenhuber, M., Poggio, T.: ‘Hierarchical models of object recognition in cortex’, Nat. Neurosci., 1999, 2, (11), p. 1019.
    19. 19)
      • 21. ‘Makeup datasets’. Available at http://www.antitza.com/makeup-datasets.html, accessed 6 March 2018.
    20. 20)
      • 18. Hong, T., Kingsbury, N., Furman, M. D.: ‘Biologically-inspired object recognition system with features from complex wavelets’. 2011 18th IEEE Int. Conf. on Image Processing (ICIP), Brussels, Belgium, 2011, pp. 261–264.
    21. 21)
      • 12. Hu, J., Ge, Y., Lu, J., et al: ‘Makeup-robust face verification’. 2013 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, Canada, 2013, pp. 2342–2346.
    22. 22)
      • 16. Lampl, I., Ferster, D., Poggio, T., et al: ‘Intracellular measurements of spatial integration and the MAX operation in complex cells of the cat primary visual cortex’, J. Neurophysiol., 2004, 92, (5), pp. 2704–2713.
    23. 23)
      • 20. Serre, T., Wolf, L., Bileschi, S., et al: ‘Robust object recognition with cortex-like mechanisms’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (3), pp. 411–426.
    24. 24)
      • 17. Serre, T., Wolf, L., Poggio, T.: ‘Object recognition with features inspired by visual cortex’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition. CVPR 2005, San Diego, CA, USA, 2005, vol. 2, pp. 994–1000.
    25. 25)
      • 2. Eckert, M.-L., Kose, N., Dugelay, J.-L.: ‘Facial cosmetics database and impact analysis on automatic face recognition’. 2013 IEEE 15th Int. Workshop on Multimedia Signal Processing (MMSP), Santa Margherita di Pula, Sardinia, Italy, 2013, pp. 434–439.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2018.5059
Loading

Related content

content/journals/10.1049/iet-bmt.2018.5059
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address