Hierarchical Aggregation based Deep Aging Feature for Age Prediction
Paper i proceeding, 2015

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.


J. Y. Qiu

Australian National University

Y. C. Dai

Australian National University

Yuhang Zhang

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

J. M. Alvarez

CSIRO Data61

2015 International Conference on Digital Image Computing: Techniques and Applications (Dicta)

978-1-4673-6795-0 (ISBN)


Elektroteknik och elektronik





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