Age interval and gender prediction using PARAFAC2 and SVMs based on visual and aural features

Age interval and gender prediction using PARAFAC2 and SVMs based on visual and aural features

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Parallel factor analysis 2 (PARAFAC2) is employed to reduce the dimensions of visual and aural features and provide ranking vectors. Subsequently, score level fusion is performed by applying a support vector machine (SVM) classifier to the ranking vectors derived by PARAFAC2 to make gender and age interval predictions. The aforementioned procedure is applied to the Trinity College Dublin Speaker Ageing database, which is supplemented with face images of the speakers and two single-modality benchmark datasets. Experimental results demonstrate the advantage of using combined aural and visual features for both prediction tasks.


    1. 1)
      • 1. Lanitis, A.: ‘A survey of the effects of aging on biometric identity verification’, Int. J. Biometrics, 2010, 2, (1), pp. 3452.
    2. 2)
      • 2. Kinnunen, T., Li, H.: ‘An overview of text-independent speaker recognition: from features to supervectors’, Speech Commun., 2010, 52, (1), pp. 1240.
    3. 3)
      • 3. Harshman, R.A.: ‘PARAFAC2: mathematical and technical notes’. UCLA Working Papers in Phonetics, 1972, vol. 22, pp. 3047.
    4. 4)
      • 4. Pantraki, E., Kotropoulos, C., Lanitis, A.: ‘Age interval and gender prediction using PARAFAC2 applied to speech utterances’. Proc. Int. Workshop Biometrics and Forensics, Limassol, Cyprus, March 2016, pp. 16.
    5. 5)
      • 5. Polastro, M.C., Eleuterio, P.M.S.: ‘Nudetective: A forensic tool to help combat child pornography through automatic nudity detection’. Proc. IEEE Int. Workshop Database and Expert Systems Applications, Bilbao, Spain, August 2010, pp. 349353.
    6. 6)
      • 6. Kelly, F., Drygajlo, A., Harte, N.: ‘Speaker verification with long-term ageing data’. Proc. IARP Int. Conf. Biometrics, New Delhi, India, March 2012, pp. 478483.
    7. 7)
      • 7. Panis, G., Lanitis, A., Tsapatsoulis, N., et al: ‘Overview of research on facial ageing using the FG-NET ageing database’, IET Biometrics, 2016, 5, (2), pp. 3746.
    8. 8)
      • 8. NIST Multimodal Information Group: ‘NIST 2008 speaker recognition evaluation test set’ (Linguistic Data Consortium, Philadelphia, US, 2011).
    9. 9)
      • 9. Kelly, F., Harte, N.: ‘Effects of long-term ageing on speaker verification’. Biometrics and ID Management, 2011 (LNCS, 6583), pp. 113124.
    10. 10)
      • 10. Kelly, F., Saeidi, R., Harte, N., et al: ‘Effect of long-term ageing on i-vector speaker verification’. Proc. Interspeech, Singapore, September 2014, pp. 8690.
    11. 11)
      • 11. Sadjadi, S.O., Ganapathy, S., Pelecanos, J.W.: ‘Speaker age estimation on conversational telephone speech using senone posterior based i-vectors’. Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, Shanghai, China, March 2016, pp. 50405044.
    12. 12)
      • 12. Liu, H., Sun, X.: ‘A partial least squares based ranker for fast and accurate age estimation’. Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, Shanghai, China, March 2016, pp. 27922796.
    13. 13)
      • 13. Geng, X., Zhou, Z.H., Smith-Miles, K.: ‘Automatic age estimation based on facial aging patterns’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (12), pp. 22342240.
    14. 14)
      • 14. Nixon, M.S., Correia, P.L., Nasrollahi, K., et al: ‘On soft biometrics’, Pattern Recognit. Lett., 2015, 68, pp. 218230.
    15. 15)
      • 15. Arigbabu, O.A., Ahmad, S.M.S., Adnan, W.A.W., et al: ‘Recent advances in facial soft biometrics’, Vis. Comput., 2015, 31, (5), pp. 513525.
    16. 16)
      • 16. Liu, L., Liu, J., Cheng, J.: ‘Age-group classification of facial images’. Proc. IEEE Int. Conf. Machine Learning and Applications, Boca Raton, FL, USA, December 2012, pp. 693696.
    17. 17)
      • 17. Guo, G., Mu, G., Fu, Y., et al: ‘Human age estimation using bio-inspired features’. Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition, Miami, FL, USA, June 2009, pp. 112119.
    18. 18)
      • 18. Chao, W.L., Liu, J.Z., Ding, J.J.: ‘Facial age estimation based on label-sensitive learning and age-oriented regression’, Pattern Recognit., 2013, 46, (3), pp. 628641.
    19. 19)
      • 19. Levi, G., Hassner, T.: ‘Age and gender classification using convolutional neural networks’. Proc. IEEE Int. Conf. Computer Vision and Pattern Recognition Workshops, Boston, MA, USA, June 2015, pp. 3442.
    20. 20)
      • 20. Bekhouche, S.E., Ouafi, A., Benlamoudi, A., et al: ‘Facial age estimation and gender classification using multi level local phase quantization’. Proc. IEEE Int. Conf. Control, Engineering & Information Technology, Tlemcen, Algeria, May 2015, pp. 14.
    21. 21)
      • 21. Mesgarani, N., Slaney, M., Shamma, S.A.: ‘Discrimination of speech from nonspeech based on multiscale spectro-temporal modulations’, IEEE Trans. Audio Speech Lang. Process., 2006, 14, (3), pp. 920930.
    22. 22)
      • 22. Panagakis, Y., Kotropoulos, C.L., Arce, G.R.: ‘Music genre classification via joint sparse low-rank representation of audio features’, IEEE/ACM Trans. Audio Speech Lang. Process., 2014, 22, (12), pp. 19051917.
    23. 23)
      • 23. Chew, P.A., Bader, B.W., Kolda, T.G., et al: ‘Cross-language information retrieval using PARAFAC2’. Proc. ACM Int. Conf. Knowledge Discovery and Data Mining, San Jose, CA, USA, August 2007, pp. 143152.
    24. 24)
      • 24. Chang, C.C., Lin, C.J.: ‘LIBSVM: A library for support vector machines’, ACM Trans. Intell. Syst. Technol., 2011, 2, (3), pp. 27:127:27.
    25. 25)
      • 25. Turnbull, D., Barrington, L., Torres, D., et al: ‘Semantic annotation and retrieval of music and sound effects’, IEEE Trans. Audio Speech Lang. Process., 2008, 16, (2), pp. 467476.
    26. 26)
      • 26. Yan, S., Wang, H., Tang, X., et al: ‘Learning auto-structured regressor from uncertain nonnegative labels’. Proc. IEEE Int. Conf. Computer Vision, Rio de Janeiro, Brazil, October 2007, pp. 18.
    27. 27)
      • 27. Lanitis, A., Draganova, C., Christodoulou, C.: ‘Comparing different classifiers for automatic age estimation’, IEEE Trans. Syst. Man Cybern. B, Cybern., 2004, 34, (1), pp. 621628.
    28. 28)
      • 28. Loizou, P.C.: ‘Speech enhancement: theory and practice’ (CRC Press, Boca Raton, FL, 2007, 2nd edn. 2013).
    29. 29)
      • 29. Goutte, C., Gaussier, E.: ‘A probabilistic interpretation of precision, recall and F-score, with implication for evaluation’. Proc. Eur. Conf. Information Retrieval Research, Santiago de Compostela, Spain, March 2005, pp. 345359.

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