The DOI matches its Crossref record.
Hybrid constraint SVR for facial age estimation
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
These authors usually assess the accuracy of their algorithms based on mathematical similarity between an age-progressed image and a target individual.
“These authors usually assess the accuracy of their algorithms based on mathematical similarity between an age-progressed image and a target individual.”
From Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data (2019) · cites this paper as [45]
Numerous studies dealing with facial age progression have recently been published, mostly by computer vision scientists.
“Numerous studies dealing with facial age progression have recently been published, mostly by computer vision scientists [13,43–46].”
From Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data (2019) · cites this paper as [45]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 18
- Fu, 2010, Age synthesis and estimation via faces, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 1955, 10.1109/TPAMI.2010.36 · DOI · Paper page
- Dehshibi, 2010, A new algorithm for age recognition from facial images, Signal Processing, 90, 2431, 10.1016/j.sigpro.2010.02.015 · DOI
- J. Lu, Y.-P. Tan, Fusing shape and texture information for facial age estimation, in: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 1477–1480. · DOI
- Wang, 2011, Automated age regression for personalized iptv services, Signal Processing, 26, 390
- T. Bocklet, A. Maier, J.G. Bauer, F. Burkhardt, E. Noth, Age and gender recognition for telephone applications based on gmm supervectors and support vector machines, in: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2008, IEEE, 2008, pp. 1605–1608. · DOI
- Lanitis, 2004, Comparing different classifiers for automatic age estimation, IEEE Transactions on Systems, Man, and Cybernetics, Part B, 34, 621, 10.1109/TSMCB.2003.817091 · DOI
- Xin, 2007, Automatic age estimation based on facial aging patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 2234, 10.1109/TPAMI.2007.70733 · DOI
- Fu, 2008, Human age estimation with regression on discriminative aging manifold, IEEE Transactions on Multimedia, 578, 10.1109/TMM.2008.921847 · DOI
- S. Yan, H. Wang, X. Tang, J. Liu, T.S. Huang, Regression from uncertain labels and its applications to soft biometrics, IEEE Transactions on Information Forensics and Security 3 (2008) 698–708. · DOI
- Guo, 2008, Image-based human age estimation by manifold learning and locally adjusted robust regression, IEEE Transactions on Image Processing, 17, 1178, 10.1109/TIP.2008.924280 · DOI
- X. Geng, K. Smith-Miles, Z. Zhou, Facial age estimation by learning from label distributions, in: 24th American Conference on Artificial Intelligence (AAAI'10), 2010. · DOI
- Duan, 2012, Visual event recognition in videos by learning from web data, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 1667, 10.1109/TPAMI.2011.265 · DOI
- B. Ni, Z. Song, S. Yan, Web image mining towards universal age estimator, in: ACM International Conference on Multimedia (MM'09), 2009, pp. 85–94. · DOI
- Duan, 2012, Domain transfer multiple kernel learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 465, 10.1109/TPAMI.2011.114 · DOI
- Lu, 2010, A novel method for gaze tracking by local pattern model and support vector regressor, Signal Processing, 90, 1290, 10.1016/j.sigpro.2009.10.014 · DOI
- Duan, 2012, Domain adaptation from multiple sources, IEEE Transactions on Neural Networks and Learning Systems, 23, 504, 10.1109/TNNLS.2011.2178556 · DOI
- M. Everingham, J. Sivic, A. Zisserman, Hello! my name is... buffy – automatic naming of characters in tv video, in: British Machine Vision Conference (BMVC'06), 2006. · DOI
- Zheng, 2007, Gabor feature-based face recognition using supervised locality preserving projection, Signal Processing, 87, 2473, 10.1016/j.sigpro.2007.03.006 · DOI
Cited by
Sorted by citations
Koudelová, J., Hoffmannová, E., Dupej, J., & Velemínská, J. (2019) · PLOS ONE · doi:10.1371/journal.pone.0212618 · Cited by 33
Metadata
- Authors
- Liu, J., Ma, Y., Duan, L., Wang, F., & Liu, Y.
- Venue
- Signal Processing
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 94
- Pages
- 576-582
- ISSN
- 0165-1684
- Cited by
- 26
- References
- 0
- Language
- en
- Funders
- National Key Research and Development Program of China (2012CB316400)
- National Natural Science Foundation of China (61273366, 60903122, 91120009)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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