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Biologically inspired makeup detection system with application in face recognition

Biologically inspired makeup detection system with application in face recognition

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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.

References

    1. 1)
      • 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. 391398.
    2. 2)
      • 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. 434439.
    3. 3)
      • 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. 726733.
    4. 4)
      • 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. 425435.
    5. 5)
      • 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. 405408.
    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. 399458.
    7. 7)
      • 7. Guo, G., Wen, L., Yan, S.: ‘Face authentication with makeup changes’, IEEE Trans. Circuits Syst. Video Technol., 2014, 24, (5), pp. 814825.
    8. 8)
      • 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. 18.
    9. 9)
      • 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. 18.
    10. 10)
      • 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.
    11. 11)
      • 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. 112119.
    12. 12)
      • 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. 23422346.
    13. 13)
      • 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. 16.
    14. 14)
      • 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. 423427.
    15. 15)
      • 15. Riesenhuber, M., Poggio, T.: ‘Hierarchical models of object recognition in cortex’, Nat. Neurosci., 1999, 2, (11), p. 1019.
    16. 16)
      • 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. 27042713.
    17. 17)
      • 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. 9941000.
    18. 18)
      • 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. 261264.
    19. 19)
      • 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.
    20. 20)
      • 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. 411426.
    21. 21)
      • 21. Makeup datasets’. Available at http://www.antitza.com/makeup-datasets.html, accessed 6 March 2018.
    22. 22)
      • 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. 886893.
    23. 23)
      • 23. Wold, S., Esbensen, K., Geladi, P.: ‘Principal component analysis’, Chemom. Intell. Lab. Syst., 1987, 2, (1–3), pp. 3752.
    24. 24)
      • 24. Gunn, S. R.: ‘Support vector machines for classification and regression’, ISIS Technol. Rep., 1998, 14, (1), pp. 516.
    25. 25)
      • 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. 131137.
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