Learning ordinal discriminative features for age estimation

By Changsheng Li, Qingshan Liu, J. Liu, Hanqing Lu. 2012

135 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Sebastian Raschka
2. Vahid Mirjalili
3. Jian Cheng
4. Arun A. Ross
5. Xinbo Gao
6. Gang Hua
7. Petros Elia
8. Zhenxing Niu

Citations in the corpus

• Quantized Convolutional Neural Networks for Mobile Devices (2015, background): “In recent years, we have witnessed the great success of convolutional neural networks (CNN) [19] in a wide range of visual applications, including image classification [16, 27], object detection [10, 9], age estimation [24, 23], etc.”
• 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).”
• Ordinal Regression with Multiple Output CNN for Age Estimation (2016, methodology): “And hence the age estimation is cast as a ordinal regression problem [4][6][32][20].”
• What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics (2016, background): “[156] who performed age-manifold learning under locality preserving requirements as well as under ordinal requirements.”