Abstract
An automatic estimation of age from face images is gaining attention due to its interesting applications such as age-based access control, customer profiling for targeted advertisements and video surveillance. However, age estimation from a face image is challenging due to complex interpersonal biological aging process, incomplete databases and dependency of facial aging on extrinsic and intrinsic factors. The published literature on age estimation utilizes multiple existing feature descriptors and then combines them into a hybrid feature vector. There is still an absence of specially designed aging feature descriptor which encodes facial aging cues. To address this issue we propose aging feature descriptor; Local Direction and Moment Pattern (LDMP), which capture directional and textural variations due to aging. We encode the orientation information available in eight unique directions. The texture is embedded into the magnitudes of higher order moments which we extract using local Tchebichef moments. Next, orientation and texture information is combined into a robust feature descriptor. To learn the age estimator, we apply warped Gaussian process regression on the proposed feature vector. Experimental analysis demonstrates the effectiveness of the proposed method on two large databases FG-NET and MORPH-II.
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References
Ahonen T, Rahtu E, Ojansivu V, Heikkila J (2008) Recognition of blurred faces using local phase quantization. In: 19th International conference on pattern recognition, 2008. ICPR 2008. IEEE, pp 1–4
Bigun J, du Buf JH (1994) N-folded symmetries by complex moments in Gabor space and their application to unsupervised texture segmentation. IEEE Trans Pattern Anal Mach Intell 16(1):80–87
Budka M, Gabrys B (2013) Density-preserving sampling: robust and efficient alternative to cross-validation for error estimation. IEEE Trans Neural Netw Learn Syst 24(1):22–34
Chang KY, Chen CS (2015) A learning framework for age rank estimation based on face images with scattering transform. IEEE Trans Image Process 24(3):785–798
Chang KY, Chen CS, Hung YP (2011) Ordinal hyperplanes ranker with cost sensitivities for age estimation. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 585–592
Chihara TS (2011) An introduction to orthogonal polynomials. Courier Corporation
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
Chu Y, Zhao L, Ahmad T (2018) Multiple feature subspaces analysis for single sample per person face recognition. Vis Comput, 1–18
Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 23(6):681–685
Dietterich T (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neur Comput 10(7):1895–1923
Farage M, Miller K, Elsner P, Maibach H (2008) Intrinsic and extrinsic factors in skin ageing: a review. Int J Cosmet Sci 30(2):87–95
Faraji MR, Qi X (2015) Face recognition under illumination variations based on eight local directional patterns. IET Biometrics 4(1):10–17
Feng S, Lang C, Feng J, Wang T, Luo J (2017) Human facial age estimation by cost-sensitive label ranking and trace norm regularization. IEEE Trans Multimed 19(1):136–148
Fernández C., Huerta I, Prati A (2015) A comparative evaluation of regression learning algorithms for facial age estimation. In: Face and facial expression recognition from real world videos. Springer, pp 133–144
Flusser J, Zitova B, Suk T (2009) Moments and moment invariants in pattern recognition. Wiley
Geng X, Yin C, Zhou ZH (2013) Facial age estimation by learning from label distributions. IEEE Trans Pattern Anal Mach Intell 35(10):2401–2412
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
Günay A, Nabiyev V (2018) A new facial age estimation method using centrally overlapped block based local texture features. Multimed Tools Appl 77(6):6555–6581
Guo G, Mu G (2011) Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 657–664
Guo G, Mu G (2013) Joint estimation of age, gender and ethnicity: Cca vs. pls. In: 2013 10th IEEE international conference and workshops on Automatic face and gesture recognition (fg). IEEE, pp 1–6
Guo G, Mu G, Fu Y, Huang TS (2009) Human age estimation using bio-inspired features. In: IEEE Conference on computer vision and pattern recognition, 2009. CVPR 2009. IEEE, pp 112–119
Han H, Otto C, Liu X, Jain AK (2015) Demographic estimation from face images: human vs. machine performance. IEEE Trans Pattern Anal Mach Intell 37 (6):1148–1161
Haralick RM (1987) Digital step edges from zero crossing of second directional derivatives. In: Readings in computer vision. Elsevier, pp 216–226
Hu MK (1962) Visual pattern recognition by moment invariants. IRE Trans Inform Theory 8(2):179–187
Huerta I, Fernández C, Prati A (2014) Facial age estimation through the fusion of texture and local appearance descriptors. In: European conference on computer vision. Springer, pp 667–681
Jabid T, Kabir MH, Chae O (2010) Local directional pattern (LDP) for face recognition. In: 2010 Digest of technical papers international conference on consumer electronics (ICCE). IEEE, pp 329–330
Khotanzad A, Hong YH (1990) Invariant image recognition by Zernike moments. IEEE Trans Pattern Anal Mach Intell 12(5):489–497
Kirsch RA (1971) Computer determination of the constituent structure of biological images. Computs Biomed Res 4(3):315–328
Knuth DE (2007) Seminumerical algorithms
Kwon YH, da Vitoria Lobo N (1999) Age classification from facial images. Comput Vis Image Understand 74(1):1–21
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
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
Li Y, Peng Z, Liang D, Chang H, Cai Z (2016) Facial age estimation by using stacked feature composition and selection. Vis Comput 32(12):1525–1536
Liao SX, Pawlak M (1996) On image analysis by moments. IEEE Trans Pattern Anal Mach Intell 18(3):254–266
Ling H, Soatto S, Ramanathan N, Jacobs DW (2007) A study of face recognition as people age. In: IEEE 11th International conference on computer vision, 2007. ICCV 2007. IEEE, pp 1–8
Lu J, Liong VE, Zhou J (2015) Cost-sensitive local binary feature learning for facial age estimation. IEEE Trans Image Process 24(12):5356–5368
Marcos JV, Cristóbal G (2013) Texture classification using discrete tchebichef moments. JOSA A 30(8):1580–1591
Mitchell T, Buchanan B, DeJong G, Dietterich T, Rosenbloom P, Waibel A (1990) Machine learning. Ann Rev Comput Sci 4(1):417–433
Mukundan R (2014) Local tchebichef moments for texture analysis
Mukundan R, Ong S, Lee PA (2001) Image analysis by Tchebichef moments. IEEE Trans Image Process 10(9):1357–1364
Ouloul IM, Moutakki Z, Afdel K, Amghar A (2018) Improvement of age estimation using an efficient wrinkles descriptor. Multimed Tools Appl, 1–35
Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The feret evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22 (10):1090–1104
Pontes JK, Britto Jr AS, Fookes C, Koerich AL (2016) A flexible hierarchical approach for facial age estimation based on multiple features. Pattern Recogn 54:34–51
Prewitt JM (1970) Object enhancement and extraction. Picture Process Psychopictorics 10(1):15–19
Ramanathan N, Chellappa R (2006) Modeling age progression in young faces. In: 2006 IEEE Computer society conference on computer vision and pattern recognition, vol 1. IEEE, pp 387–394
Rasmussen C (2006) Cki williams gaussian processes for machine learning mit press. Cambridge
Ricanek K, Tesafaye T (2006) Morph: a longitudinal image database of normal adult age-progression. In: 7th International conference on automatic face and gesture recognition, 2006. FGR 2006. IEEE, pp 341–345
Rivera AR, Castillo JR, Chae OO (2013) Local directional number pattern for face analysis: face and expression recognition. IEEE Trans Image Process 22(5):1740–1752
Rivera AR, Castillo JR, Chae O (2015) Local directional texture pattern image descriptor. Pattern Recogn Lett 51:94–100
Snelson E, Ghahramani Z, Rasmussen CE (2004) Warped gaussian processes. In: Advances in neural information processing systems, pp 337–344
Suo J, Wu T, Zhu S, Shan S, Chen X, Gao W (2008) Design sparse features for age estimation using hierarchical face model. In: 8th IEEE International conference on automatic face & gesture recognition, 2008. FG’08. IEEE, pp 1–6
Teh CH, Chin RT (1988) On image analysis by the methods of moments. IEEE Trans Pattern Ana Mach Intell 10(4):496–513
The fg-net aging database. http://www.fgnet.rsunit.com/
Thukral P, Mitra K, Chellappa R (2012) A hierarchical approach for human age estimation. In: 2012 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1529–1532
Wang M, Knoesen A (2007) Rotation-and scale-invariant texture features based on spectral moment invariants. JOSA A 24(9):2550–2557
Wang S, Tao D, Yang J (2016) Relative attribute svm+ learning for age estimation. IEEE Trans Cybern 46(3):827–839
Wee CY, Paramesran R, Mukundan R, Jiang X (2010) Image quality assessment by discrete orthogonal moments. Pattern Recogn 43(12):4055–4068
Weng R, Lu J, Yang G, Tan YP (2013) Multi-feature ordinal ranking for facial age estimation. In: 2013 10th IEEE international conference and workshops on automatic face and gesture recognition (FG). IEEE, pp 1–6
Wu T, Turaga P, Chellappa R (2012) Age estimation and face verification across aging using landmarks. IEEE Trans Inf Forens Secur 7(6):1780–1788
Yap PT, Raveendran P (2004) Image focus measure based on chebyshev moments. IEE Proc-Vis Image Signal Process 151(2):128–136
Yap PT, Paramesran R, Ong SH (2003) Image analysis by Krawtchouk moments. IEEE Trans Image Process 12(11):1367–1377
Zhang Y, Yeung DY (2010) Multi-task warped gaussian process for personalized age estimation. In: 2010 IEEE Conference on computer vision and pattern recognition (CVPR). IEEE, pp 2622–2629
Zhao W, Krishnaswamy A, Chellappa R, Swets DL, Weng J (1998) Discriminant analysis of principal components for face recognition. In: Face recognition. Springer, pp 73–85
Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: A literature survey. ACM Comput Surveys (CSUR) 35(4):399–458
Zhu K, Gong D, Li Z, Tang X (2014) Orthogonal Gaussian process for automatic age estimation. In: Proceedings of the 22nd ACM international conference on multimedia. ACM, pp 857–860
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Sawant, M., Addepalli, S. & Bhurchandi, K. Age estimation using local direction and moment pattern (LDMP) features. Multimed Tools Appl 78, 30419–30441 (2019). https://doi.org/10.1007/s11042-019-7589-1
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DOI: https://doi.org/10.1007/s11042-019-7589-1