Abstract
Age-related research has become an attractive topic in recent years due to its wide range of application scenarios. In spite of the great advancement in face related works in recent years, face recognition across ages is still a challenging problem. In this paper, we propose a new deep Convolutional Neural Network (CNN) model for age-invariant face verification, which can learn features, distance metrics and threshold simultaneously. We also introduce two tricks to overcome insufficient memory capacity issue and to reduce computational cost. Experimental results show our method outperforms other state-of-the-art methods on MORPH-II database, which improves the rank-1 recognition rate from the current best performance 92.80% to 93.6%.
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Chen, D., Cao, X., Wen, F., Sun, J.: Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification. In: CVPR (2013)
Lu, C., Tang, X.: Surpassing human-level face verification performance on LFW with GaussianFace. ArXiv e-prints (2014)
Sun, Y., Chen, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification. In: NIPS (2014)
Fu, Y., Guo, G., Huang, T.S.: Age synthesis and estimation via faces: A survey. TPAMI 32(11), 1955–1976 (2010)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. TPAMI 19(7), 711–720 (1997)
Moghaddam, B., Jebara, T., Pentland, A.: Bayesian face recognition. Pattern Recognition 33, 1771–1782 (2000)
Chen, D., Cao, X., Wang, L., Wen, F., Sun, J.: Bayesian face revisited: a joint formulation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 566–579. Springer, Heidelberg (2012)
Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? Metric learning approaches for face identification. In: ICCV (2009)
Li, Z., Chang, S., Liang, F., Huang, T.S., Cao, L., Smith, J.R.: Learning locally-adaptive decision functions for person verification. In: CVPR (2013)
Lanitis, A., Taylor, C.J., Cootes, T.: Toward automatic simulation of aging effects on face images. TPAMI 24(4), 442–455 (2002)
Geng, X., Zhou, Z.H., Smith-Miles, K.: Automatic age estimation based on facial aging patterns. TPAMI 29(12), 2234–2240 (2007)
Fu, Y., Huang, T.S.: Human age estimation with regression on discriminative aging manifold. TMM 10(4), 578–584 (2008)
Guo, G., Mu, G., Fu, Y., Huang, T.: Human age estimation using bio-inspired features. In: CVPR (2009)
Geng, X., Yin, C., Zhou, Z.H.: Facial age estimation by learning from label distributions. TPAMI 35(10), 2401–2412 (2013)
Guo, G., Zhang, C.: A study on cross-population age estimation. In: CVPR (2014)
Geng, X., Wang, Q., Xia, Y.: Facial age estimation by adaptive label distribution learning. In: ICPR (2014)
Li, Y., Peng, Z., Liang, D., Chang, H. and Cai, Z.: Facial age estimation by using stacked feature composition and selection. The Visual Computer, 1–12 (2015)
Yan, C., Lang, C., Wang, T., Du, X., Zhang, C.: Age estimation based on convolutional neural network. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, C.-K., Huet, B., Ngo, C.-W. (eds.) PCM 2014. LNCS, vol. 8879, pp. 211–220. Springer, Heidelberg (2014)
Park, U., Tong, Y., Jain, A.K.: Age-invariant face recognition. TPAMI 32(5), 947–954 (2010)
Ling, H., Soatto, S., Ramanathan, N., Jacobs, D.W.: Face verification across age progression using discriminative methods. TIFS 5(1), 82–91 (2010)
Li, Z., Park, U., Jain, A.K.: A discriminative model for age invariant face recognition. TIFS 6(3–2), 1028–1037 (2011)
Gong, D., Li, Z., Lin, D., Liu, J., Tang, X.: Hidden factor analysis for age invariant face recognition. In: ICCV (2013)
Chen, B.-C., Chen, C.-S., Hsu, W.H.: Cross-age reference coding for age-invariant face recognition and retrieval. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VI. LNCS, vol. 8694, pp. 768–783. Springer, Heidelberg (2014)
Xing, E.P., Jordan, M.I., Russell, S., Ng, A.Y.: Distance metric learning with application to clustering with side-information. In: NIPS (2002)
Ricanek, K., Tesafaye, T.: Morph: a longitudinal image database of normal adult age-progression. In: FG (2006)
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Li, Y., Wang, G., Lin, L., Chang, H. (2015). A Deep Joint Learning Approach for Age Invariant Face Verification. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48558-3_30
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DOI: https://doi.org/10.1007/978-3-662-48558-3_30
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