Using Ranking-CNN for Age Estimation
| Autor: | Jialiang Le, Shixing Chen, Caojin Zhang, Mike Rao, Ming Dong |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
021110 strategic
defence & security studies Artificial neural network Computer science business.industry Feature extraction 0211 other engineering and technologies Pattern recognition 02 engineering and technology Machine learning computer.software_genre Facial recognition system Convolutional neural network Support vector machine Ranking 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Feature learning |
| Zdroj: | CVPR |
| DOI: | 10.1109/cvpr.2017.86 |
| Popis: | Human age is considered an important biometric trait for human identification or search. Recent research shows that the aging features deeply learned from large-scale data lead to significant performance improvement on facial image-based age estimation. However, age-related ordinal information is totally ignored in these approaches. In this paper, we propose a novel Convolutional Neural Network (CNN)-based framework, ranking-CNN, for age estimation. Ranking-CNN contains a series of basic CNNs, each of which is trained with ordinal age labels. Then, their binary outputs are aggregated for the final age prediction. We theoretically obtain a much tighter error bound for ranking-based age estimation. Moreover, we rigorously prove that ranking-CNN is more likely to get smaller estimation errors when compared with multi-class classification approaches. Through extensive experiments, we show that statistically, ranking-CNN significantly outperforms other state-of-the-art age estimation models on benchmark datasets. |
| Databáze: | OpenAIRE |
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