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Improvement of age estimation using an efficient wrinkles descriptor

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Abstract

Lately, automatic age estimation from facial images is in demand because of the usage of this technique in different fields including security, demographic analysis, access control and vending machines control. However, age estimation is difficult to conduct due to the aging process features’ evolution complexity, such as the face shape and skin wrinkles. In this context, we propose a new descriptor called Local Matched Filter Binary Pattern (LMFBP) designed specifically for the detection and extraction of skin wrinkles. This descriptor is based on exploiting both the Matched Filter and the texture operator Local Binary Pattern (LBP). The Matched Filter handles the detection of wrinkles using template matching between the approximate shape of wrinkles and the face image patches. Furthermore, the LBP operator encodes the response of the Matched Filter into pattern codes to build the histogram of skin aging feature. The fusion of local features provided by the LMFBP with the global features of the appearance enabled us to propose a new age estimation method. In this method, we adopted the hierarchical approach in the learning phase, in order to consider the varying aging process from one age stage to another. The proposed age estimation method has been tested on both FGnetAD, HQfaces and PAL datasets, and the results provided are 4.95, 3.65 and 5.33 in terms of MAE, respectively. These results prove the efficiency of the proposed approach when compared to the state-of-the-art age estimation methods.

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References

  1. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: Application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041. https://doi.org/10.1109/TPAMI.2006.244

    Article  MATH  Google Scholar 

  2. Bukar AM, Ugail H, Connah D (2016) Automatic age and gender classification using supervised appearance model. J Electron Imaging 25(6):061,605–061,605. https://doi.org/10.1117/1.JEI.25.6.061605

    Article  Google Scholar 

  3. Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M (1989) Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imaging 8(3):263–269. https://doi.org/10.1109/42.34715

    Article  Google Scholar 

  4. Choi SE, Lee YJ, Lee SJ, Park KR, Kim J (2011) Age estimation using a hierarchical classifier based on global and local facial features. Pattern Recogn 44(6):1262–1281. https://doi.org/10.1016/j.patcog.2010.12.005

    Article  MATH  Google Scholar 

  5. Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59. https://doi.org/10.1006/cviu.1995.1004

    Article  Google Scholar 

  6. Cootes TF, Edwards GJ, Taylor CJ (1998) Active appearance models. In: European conference on computer vision. Springer, pp 484–498

  7. Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 23(6):681–685

    Article  Google Scholar 

  8. Cula GO, Bargo PR, Nkengne A, Kollias N (2013) Assessing facial wrinkles: automatic detection and quantification. Skin Res Technol 19(1):e243–e251

    Article  Google Scholar 

  9. Dong Y, Liu Y, Lian S (2016) Automatic age estimation based on deep learning algorithm. Neurocomputing 187:4–10. https://doi.org/10.1016/j.neucom.2015.09.115

    Article  Google Scholar 

  10. El Dib MY, El-Saban M (2010) Human age estimation using enhanced bio-inspired features (ebif). In: 17th IEEE International Conference on Image Processing(ICIP’10). IEEE, pp 1589–1592

  11. Fu Y, Xu Y, Huang TS (2007) Estimating human age by manifold analysis of face pictures and regression on aging features. In: IEEE International Conference on Multimedia and Expo. IEEE, pp 1383–1386

  12. Fu Y, Guo G, Huang TS (2010) Age synthesis and estimation via faces: a survey. IEEE Trans Pattern Anal Mach Intell 32(11):1955–1976

    Article  Google Scholar 

  13. Fukai H, Takimoto H, Mitsukura Y, Fukumi M (2007) Apparent age estimation system based on age perception. In: SICE, 2007 Annual Conference. IEEE, pp 2808–2812

  14. Gao F, Ai H (2009) Face age classification on consumer images with gabor feature and fuzzy lda method. In: International Conference on Biometrics. Springer, pp 132–141

  15. Geng X, Zhou ZH, Smith-Miles K (2007) Automatic age estimation based on facial aging patterns. IEEE Trans Pattern Anal Mach Intell 29(12):2234–2240

    Article  Google Scholar 

  16. Ghufran RS, Leu JS, Prakosa SW (2016) Improving the age estimation accuracy by a hybrid optimization scheme. Multimedia Tools and Applications 77:1–17. https://doi.org/10.1007/s11042-017-4397-3

    Google Scholar 

  17. Günay A, Nabiyev V (2017) A new facial age estimation method using centrally overlapped block based local texture features. Multimedia Tools and Applications 77:1–27. https://doi.org/10.1007/s11042-017-4572-6

    Google Scholar 

  18. Gunay A, Nabiyev VV (2008) Automatic age classification with lbp. In: 23rd International Symposium on Computer and Information Sciences, 2008. ISCIS’08. IEEE, pp 1–4. https://doi.org/10.1109/ISCIS.2008.4717926

  19. Guo G, Fu Y, Dyer CR, Huang TS (2008a) A probabilistic fusion approach to human age prediction. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops(CVPRW’08). IEEE, pp 1–6. https://doi.org/10.1109/CVPRW.2008.4563041

  20. Guo G, Fu Y, Huang TS, Dyer CR (2008b) Locally adjusted robust regression for human age estimation. In: 2008. WACV 2008. IEEE Workshop on Applications of computer vision. IEEE, pp 1-6

  21. Guo G, Mu G, Fu Y, Dyer C, Huang T (2009a) A study on automatic age estimation using a large database. In: IEEE 12th International Conference on Computer Vision. IEEE, pp 1986–1991

  22. Guo G, Mu G, Fu Y, Huang TS (2009b) Human age estimation using bio-inspired features. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR’09). IEEE, pp 112–119

  23. Guo G (2012) Human age estimation and sex classification. In: Video Analytics for Business Intelligence. Springer, pp 101–131. https://doi.org/10.1007/978-3-642-28598-1_4

  24. Han H, Otto C, Jain AK (2013) Age estimation from face images: Human vs. machine performance. In: 2013 International Conference on Biometrics(ICB). IEEE, pp 1–8. https://doi.org/10.1109/ICB.2013.6613022

  25. Hayashi J, Yasumoto M, Ito H, Koshimizu H (2001) Method for estimating and modeling age and gender using facial image processing. In: Seventh International Conference on Virtual Systems and Multimedia. IEEE, pp 439–448. https://doi.org/10.1109/VSMM.2001.969698

  26. Horng WB, Lee CP, Chen CW (2001) Classification of age groups based on facial features. Tamkang J Sci Eng 4(3):183–192

    Google Scholar 

  27. Huerta I, Fernández C, Segura C, Hernando J, Prati A (2015) A deep analysis on age estimation. Pattern Recogn Lett 68:239–249. https://doi.org/10.1016/j.patrec.2015.06.006

    Article  Google Scholar 

  28. Jain A, Nandakumar K, Ross A (2005) Score normalization in multimodal biometric systems. Pattern Recogn 38(12):2270–2285. https://doi.org/10.1016/j.patcog.2005.01.012

    Article  Google Scholar 

  29. Kilinc M, Akgul YS (2013) Automatic human age estimation using overlapped age groups. In: Computer Vision, Imaging and Computer Graphics. Theory and Application. Springer, pp 313–325. https://doi.org/10.1007/978-3-642-38241-3_21

  30. Kwon Y H, Lobo N (1999) Age classification from facial images. Comput Vis Image Underst 74(1):1–21. https://doi.org/10.1006/cviu.1997.0549

    Article  Google Scholar 

  31. Lai D, Chen Y, Luo X, Du J, Wang T (2017) Age estimation with dynamic age range. Multimed Tools Appl 76(5):6551–6573. https://doi.org/10.1007/s11042-015-3230-0

    Article  Google Scholar 

  32. Lanitis A (2002) On the significance of different facial parts for automatic age estimation. In: 14Th international conference on digital signal processing(DSP’02). IEEE, vol 2, pp 1027–1030

  33. Lanitis A, Taylor CJ, Cootes TF (2002) Toward automatic simulation of aging effects on face images. IEEE Trans Pattern Anal Mach Intell 24(4):442–455. https://doi.org/10.1109/34.993553

    Article  Google Scholar 

  34. Lanitis A, Draganova C, Christodoulou C (2004) Comparing different classifiers for automatic age estimation. IEEE Trans Syst Man Cybern Part B (Cybern) 34(1):621–628. https://doi.org/10.1109/TSMCB.2003.817091

    Article  Google Scholar 

  35. Lemperle G, Holmes R, Cohen S, Lemperle S (2001) A classification of facial wrinkles. Plast Reconstr Surg 108(6):1735–1750

    Article  Google Scholar 

  36. Liang Y, Wang X, Zhang L, Wang Z (2014) A hierarchical framework for facial age estimation. Math Probl Eng 2014. https://doi.org/10.1155/2014/242846

  37. Liu J, Ma Y, Duan L, Wang F, Liu Y (2014) Hybrid constraint svr for facial age estimation. Signal Process 94:576–582

    Article  Google Scholar 

  38. Luu K, Ricanek K, Bui TD, Suen CY (2009) Age estimation using active appearance models and support vector machine regression. In: IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems(BTAS’09). IEEE, pp 1–5. https://doi.org/10.1109/BTAS.2009.5339053

  39. Matthews I, Baker S (2004) Active appearance models revisited. Int J Comput Vis 60(2):135–164

    Article  Google Scholar 

  40. Minear M, Park DC (2004) A lifespan database of adult facial stimuli. Behav Res Methods Instrum Comput 36(4):630–633. https://doi.org/10.3758/BF03206543

    Article  Google Scholar 

  41. Ng CC, Yap MH, Costen N, Li B (2014a) Automatic wrinkle detection using hybrid hessian filter. In: Asian Conference on Computer Vision. Springer, pp 609–622

  42. Ng CC, Yap MH, Costen N, Li B (2014b) An investigation on local wrinkle-based extractor of age estimation. In: International conference on computer vision theory and applications(VISAPP), IEEE, vol 1, pp 675–681

  43. Nguyen DT, Cho SR, Shin KY, Bang JW, Park KR (2014) Comparative study of human age estimation with or without preclassification of gender and facial expression. The Scientific World Journal

  44. Nguyen DT, Cho SR, Pham TD, Park KR (2015) Human age estimation method robust to camera sensor and/or face movement. Sensors 15(9):21,898–21,930

    Article  Google Scholar 

  45. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29 (1):51–59. https://doi.org/10.1016/0031-3203(95)00067-4

    Article  Google Scholar 

  46. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987. https://doi.org/10.1109/TPAMI.2002.1017623

    Article  MATH  Google Scholar 

  47. Ouloul IM, Afdel K, Amghar A (2016) Age estimation using local matched filter binary pattern. In: 2016 IEEE/ACS 13Th international conference of computer systems and applications (AICCSA). IEEE, pp 1-5. https://doi.org/10.1109/AICCSA.2016.7945649

  48. Panis G, Lanitis A, Tsapatsoulis N, Cootes TF (2016) Overview of research on facial ageing using the fg-net ageing database. IET Biom 5(2):37–46. https://doi.org/10.1049/iet-bmt.2014.0053

    Article  Google Scholar 

  49. Pietikäinen M, Ojala T, Xu Z (2000) Rotation-invariant texture classification using feature distributions. Pattern Recogn 33(1):43–52. https://doi.org/10.1016/S0031-3203(99)00032-1

    Article  Google Scholar 

  50. Pontes JK, Britto AS, Fookes C, Koerich AL (2016) A flexible hierarchical approach for facial age estimation based on multiple features. Pattern Recogn 54:34–51

    Article  Google Scholar 

  51. Ross AA, Govindarajan R (2005) Feature level fusion of hand and face biometrics. In: Defense and Security, International Society for Optics and Photonics, pp 196–204. https://doi.org/10.1117/12.606093

  52. Thukral P, Mitra K, Chellappa R (2012) A hierarchical approach for human age estimation. In: IEEE International conference on acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 1529–1532. https://doi.org/10.1109/ICASSP.2012.6288182

  53. Txia JD, Huang CL (2009) Age estimation using aam and local facial features. In: Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing. IEEE, pp 885–888. https://doi.org/10.1109/IIH-MSP.2009.142

  54. Vieira TF, Bottino A, Laurentini A, De Simone M (2014) Detecting sibli‘ngs in image pairs. Vis Comput 30(12):1333–1345. https://doi.org/10.1007/s00371-013-0884-3

    Article  Google Scholar 

  55. Wang X, Guo R, Kambhamettu C (2015) Deeply-learned feature for age estimation. In: 2015 IEEE Winter Conference on Applications of computer vision (WACV). IEEE, pp 534–541

  56. Wiskott L, Krüger N, Kuiger N, Von Der Malsburg C (1997) Face recognition by elastic bunch graph matching. IEEE Transactions on pattern analysis and machine intelligence

  57. Yan S, Wang H, Tang X, Huang TS (2007) Learning auto-structured regressor from uncertain nonnegative labels. In: IEEE 11th International Conference on Computer Vision(ICCV’07). IEEE, pp 1–8

  58. Yang M, Zhu S, Lv F, Yu K (2011) Correspondence driven adaptation for human profile recognition. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR’11). IEEE, pp 505–512

  59. Ylioinas J, Hadid A, Hong X, Pietikäinen M (2013) Age estimation using local binary pattern kernel density estimate. In: International Conference on Image Analysis and Processing. Springer, pp 141–150. https://doi.org/10.1007/978-3-642-41181-6_15

  60. Zhang W, Shan S, Gao W, Chen X, Zhang H (2005) Local gabor binary pattern histogram sequence (lgbphs): a novel non-statistical model for face representation and recognition. In: Tenth IEEE international conference on computer vision(ICCV’05. IEEE, vol 1, pp 786–791. https://doi.org/10.1109/ICCV.2005.147

  61. Zhang B, Zhang L, Zhang L, Karray F (2010) Retinal vessel extraction by matched filter with first-order derivative of gaussian. Comput Biol Med 40(4):438–445

    Article  Google Scholar 

  62. Zhang L, Zhou Z, Li H (2012) Binary gabor pattern: An efficient and robust descriptor for texture classification. In: 19th IEEE International Conference on Image Processing(ICIP’12). IEEE, pp 81–84. https://doi.org/10.1109/ICIP.2012.6466800

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Authors and Affiliations

  1. Metrology and Information Processing Lab, Ibnou Zohr University, B.P 80000, Agadir, Morocco

    Imad Mohamed Ouloul, Zakaria Moutakki & Abdellah Amghar

  2. Computer Systems and Vision Lab, Ibnou Zohr University, B.P 80000, Agadir, Morocco

    Karim Afdel

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  1. Imad Mohamed Ouloul
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Correspondence to Imad Mohamed Ouloul.

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This research work was supported by the Centre National pour la Recherche Scientifique et Technique CNRST, project: PPR2 and research grant No: 010UIZ2014.

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Ouloul, I.M., Moutakki, Z., Afdel, K. et al. Improvement of age estimation using an efficient wrinkles descriptor. Multimed Tools Appl 78, 1913–1947 (2019). https://doi.org/10.1007/s11042-018-6275-z

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