Ordinal hyperplanes ranker with cost sensitivities for age estimation
By Kuang-Yu Chang, Chu-Song Chen, Yi-Ping Hung. Computer Vision and Pattern Recognition, 2011
362 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/967c7adee6b1272b8b6014beaa138f127b5943a2
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In Computer Vision and Pattern Recognition
Citations in the corpus
- Ordinal Regression Methods: Survey and Experimental Study (2016, neutral): “C´ordoba, Spain, e-mail: { pagutierrez,i82perom,jsanchezm,chervas } @uco.es F. Fern´andez-Navarro is with the Department of Mathematics and Engineering, Loyola University Andaluc´ıa, Spain, e-mail: i22fenaf@uco.es itself.”
- Face Recognition and Retrieval Using Cross-Age Reference Coding With Cross-Age Celebrity Dataset (2015, background): “Most existing age-related works for face image analysis focus on age estimation [17]–[19] and age simulation [20]–[22].”
- Demographic Estimation from Face Images: Human vs. Machine Performance (2015, neutral): “(2011) [28] AAM FG-NET (82, 1,002)”
- Facial Age Estimation by Learning from Label Distributions (2010, methodology): “In order to build a robust facial age estimation system, Ni et al. [22], [23] proposed a method based on the mining of the noisy aging face images collected from the web images and videos.”
- Ordinal Regression with Multiple Output CNN for Age Estimation (2016, methodology): “Ordinal Regression BIFs + BIFs + CNN + BIFS + BIFS + Ours LSVR [15] CCA [14] LSVR [31] OR-SVM [5] OHRank [6] which is utilized by the RSN to recommend classmates or friends to the user.”
- Rank consistent ordinal regression for neural networks with application to age estimation (2019, methodology): “Based on this assumption, age prediction can be modeled using ordinal regression-based approaches (Yang et al., 2010; Chang et al., 2011; Cao et al., 2012; Li et al., 2012).”
- Learning ordinal discriminative features for age estimation (2012, methodology): “All the above methods are about feature selection, so based on the selected features, we adopt the ordinal hyperplanes ranker (OHRank) model as the age estimator, which has been demonstrated to be a good age estimator [4].”
- Cumulative Attribute Space for Age and Crowd Density Estimation (2013, methodology): “Settings – For FG-NET, we followed the same leave-oneperson-out setting as in [7, 15, 32, 33, 35].”