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Hierarchical Aggregation based Deep Aging Feature for Age Prediction

J. Y. Qiu ; Y. C. Dai ; Yuhang Zhang (Institutionen för signaler och system, Bildanalys och datorseende) ; J. M. Alvarez
2015 International Conference on Digital Image Computing: Techniques and Applications (Dicta) p. 373-377. (2015)
[Konferensbidrag, refereegranskat]

We propose a new, hierarchical, aggregation-based deep neural network to learn aging features from facial images. Our deep-aging feature vector is designed to capture both local and global aging cues from facial images. A Convolutional Neural Network (CNN) is employed to extract region-specific features at the lowest level of our hierarchy. These features are then hierarchically aggregated to consecutive higher levels and the resultant aging feature vector, of dimensionality 110, achieves both good discriminative ability and efficiency. Experimental results of age prediction on the MORPH-II databases show that our method outperforms state-of-the-art aging features by a clear margin. Experimental trails of our method across race and gender provide further confidence in its performance and robustness.

Denna post skapades 2016-11-30. Senast ändrad 2017-07-05.
CPL Pubid: 245732


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Institutioner (Chalmers)

Institutionen för signaler och system, Bildanalys och datorseende (2013-2017)


Elektroteknik och elektronik

Chalmers infrastruktur