access icon free Deep learning for face recognition on mobile devices

© The Institution of Engineering and Technology
Received 14/05/2019, Accepted 19/12/2019, Revised 09/10/2019, Published 09/01/2020

Mobility implies a great variability of capturing conditions, which is not easy to control and directly affects to face detection and the extraction of facial features. Deep learning solutions seem to be the most interesting choice for automatic face recognition, but they are highly dependent on the model generated during the training stage. In addition, the size of the models makes it difficult for their integration into applications oriented to mobile devices, particularly when the model must be embedded. In this work, a small-size deep-learning model was trained for face recognition on low capacity devices and evaluated in terms of accuracy, size and timings to provide quantitative data. This evaluation is aimed to cover as many scenarios as possible, so different databases were employed, including public and private datasets specifically oriented to recreate the complexity of mobile scenarios. Also, publicly available models and traditional approaches were included in the evaluation to carry out a fair comparison. Moreover, given the relevance of template matching and face detection stages, the assessment is complemented with different classifiers and detectors. Finally, a JAVA-Android implementation of the system was developed and evaluated to obtain performance data of the whole system integrated on a mobile phone.

Inspec keywords: mobile computing; face recognition; Java; learning (artificial intelligence)

Other keywords: deep learning solutions; mobile scenarios; great variability; mobile phone; private datasets; capturing conditions; facial features; mobile devices; automatic face recognition; publicly available models; low capacity devices; face detection stages; public datasets; template matching; training stage; small-size deep-learning model; interesting choice

Subjects: Mobile, ubiquitous and pervasive computing; Computer vision and image processing techniques; Image recognition; Knowledge engineering techniques

References

    1. 1)
      • 39. Bradski, G.: ‘The OpenCV library’, Dr. Dobb‘S J. Softw. Tools, 2000, 25, pp. 120–125.
    2. 2)
      • 4. Mobile Pass project: Available at http://mobilepass-project.eu/, accessed 3 April 2019.
    3. 3)
      • 9. Zhou, Y., Liu, D., Huang, T.: ‘Survey of face detection on low-quality images’. 2018 13th IEEE Int. Conf. on Automatic Face Gesture Recognition (FG 2018), Xiʼnan, China, May 2018, pp. 769–773.
    4. 4)
      • 30. Jolliffe, I.T.: ‘Principal component analysis’ (Springer-Verlag, New York, 1986).
    5. 5)
      • 25. Yi, D., Lei, Z., Liao, S., et al: ‘Learning face representation from scratch’. CoRR, abs/1411.7923, 2014.
    6. 6)
      • 10. Schroff, F., Kalenichenko, D., Philbin, J.: ‘Facenet: a unified embedding for face recognition and clustering’. Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition 2015, Boston, MA, USA, 2015.
    7. 7)
      • 35. The Extended Yale Face Database B: Available at http://vision.ucsd.edu/iskwak/ExtYaleDatabase/ExtYaleB.html, accessed 3 December 2019.
    8. 8)
      • 15. International Organization for Standardization (ISO): ‘ISO/IEC 19795-2:2007: information technology – biometric performance testing and reporting – part 2: testing methodologies for technology and scenario evaluation’ (2007).
    9. 9)
      • 29. Ojala, T., Pietikäinen, M., Harwood, D.: ‘A comparative study of texture measures with classification based on featured distributions’, Pattern Recognit., 1996, 29, (1), pp. 51–59.
    10. 10)
      • 12. Huang, G.B., Mattar, M., Berg, T., et al: ‘Labeled faces in the wild: a database for studying face recognition in unconstrained environments’. Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition, Marseille, France, October 2008. Erik Learned-Miller and Andras Ferencz and Frédéric Jurie.
    11. 11)
      • 32. BioID Face Database: Available at https://www.bioid.com/facedb/, accessed 3 December 2019.
    12. 12)
      • 26. Ng, H.W., Winkler, S.: ‘A data-driven approach to cleaning large face datasets’. 2014 IEEE Int. Conf. on Image Processing (ICIP), Paris, France, October 2014, pp. 343–347.
    13. 13)
      • 21. Szegedy, C., Liu, W., Jia, Y., et al: ‘Going deeper with convolutions’. 2015 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June 2015, pp. 1–9.
    14. 14)
      • 8. Chihaoui, M., Elkefi, A., Bellil, W., Ben Amar, C.: ‘A survey of 2D face recognition techniques’, Computers2016, 5, (4), 21.
    15. 15)
      • 28. Ojala, T., Pietikainen, M., Maenpaa, T.: ‘Multiresolution gray-scale and rotation invariant texture classification with local binary patterns’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (7), pp. 971–987.
    16. 16)
      • 17. Dalal, N., Triggs, B.: ‘Histograms of oriented gradients for human detection’. 2005 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR'05), San Diego, CA, USA, June 2005, vol. 1, pp. 886–893.
    17. 17)
      • 3. ‘BBVA facial recognition payments’. Available at https://www.bbva.com/en/bbva-launches-facial-recognition-payments/, accessed 3 April 2019.
    18. 18)
      • 19. King, D.E.: ‘Dlib-ml: a machine learning toolkit’, J. Mach. Learn. Res., 2009, 10, pp. 1755–1758.
    19. 19)
      • 37. Chingovska, I., Anjos, A., Marcel, S.: ‘On the effectiveness of local binary patterns in face anti-spoofing’. 2012.
    20. 20)
      • 38. Ríos-Sánchez, B., Arriaga-Gómez, M.F., Guerra-Casanova, J., et al: ‘Gb2sμMOD: a MUltiMODal biometric video database using visible and IR light’, Inf. Fusion, 2016, 32, (Part B), pp. 64–79. SI: Information Fusion in Biometrics.
    21. 21)
      • 20. Zhang, K., Zhang, Z., Li, Z., et al: ‘Joint face detection and alignment using multitask cascaded convolutional networks’, IEEE Signal Process. Lett., 2016, 23, (10), pp. 1499–1503.
    22. 22)
      • 34. The Database of Faces: Available at http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html, accessed 3 December 2019.
    23. 23)
      • 16. Viola, P., Jones, M.: ‘Rapid object detection using a boosted cascade of simple features’. Proc. of the 2001 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, CVPR 2001, Kauai, HI, USA, 2001, vol. 1, pp. I-511–I-518.
    24. 24)
      • 31. Marquardt, D.W.: ‘An algorithm for least-squares estimation of nonlinear parameters’, SIAM J. Appl. Math., 1963, 11, (2), pp. 431–441.
    25. 25)
      • 36. Anjos, A., Marcel, S.: ‘Counter-measures to photo attacks in face recognition: a public database and a baseline’. Int. Joint Conf. on Biometrics 2011, Washington, DC, USA, October 2011.
    26. 26)
      • 1. Android biometrics: Available at https://source.android.com/security/biometric/, accessed 3 April 2019.
    27. 27)
      • 2. Face ID: Available at https://support.apple.com/en-us/HT208108, accessed 3 April 2019.
    28. 28)
      • 33. The Essex University Collection of Face Images: Available at https://cswww.essex.ac.uk/mv/allfaces/index.html, accessed 3 December 2019.
    29. 29)
      • 14. International Organization for Standardization (ISO): ‘ISO/IEC 19795-1:2007: information technology – biometric performance testing and reporting – part 1: principles and framework’ (2007).
    30. 30)
      • 18. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., et al: ‘Object detection with discriminatively trained part-based models’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, (9), pp. 1627–1645.
    31. 31)
      • 5. Automated Border Control Gates for Europe. Available at http://abc4eu.com/, accessed 3 April 2019.
    32. 32)
      • 6. iBorderCtrl Project: Available at https://www.iborderctrl.eu/The-project, accessed 3 April 2019.
    33. 33)
      • 22. Weinberger, K.Q., Blitzer, J., Saul, L.K.: ‘Distance metric learning for large margin nearest neighbor classification’ inWeiss, Y., Schölkopf, B., Platt, J.C. (Eds.): ‘Advances in neural information processing systems’, Cambridge, MA, USA, vol. 18, (MIT Press, 2006), pp. 1473–1480.
    34. 34)
      • 13. Amos, B., Bartosz, L., Satyanarayanan, M.: ‘Openface: a general-purpose face recognition library with mobile applications’. Technical report, CMU-CS-16-118, CMU School of Computer Science, 2016.
    35. 35)
      • 11. Wang, M., Deng, W.: ‘Deep face recognition: a survey’. CoRR, abs/1804.06655, 2018.
    36. 36)
      • 23. Face recognition using Tensorflow: Available at https://github.com/davidsandberg/facenet, accessed: 2019-03-08.
    37. 37)
      • 24. Taigman, Y., Yang, M., Ranzato, M., et al: ‘Deepface: closing the gap to human-level performance in face verification’. 2014 IEEE Conf. on Computer Vision and Pattern Recognition, Columbus, OH, USA, June 2014, pp. 1701–1708.
    38. 38)
      • 7. Guerra-Casanova, J., Ríos-Sánchez, B., Viana-Matesanz, M., et al: ‘Comfort and security perception of biometrics in mobile phones with widespread sensors’. 2016 IEEE 35th Symp. on Reliable Distributed Systems Workshops (SRDSW), Budapest, Hungary, September 2016, pp. 13–18.
    39. 39)
      • 27. OpenFace: ‘Free and open source face recognition with deep neural networks’. Available at https://cmusatyalab.github.io/openface/, accessed 3 August 2018.
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