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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.

A discussion of US higher education at the time of the transition from an industrial to a knowledge‐based economy. This is the time when people need to be educated to cope with not just one job for life but a series of different jobs demanding different skills and aptitudes. Universities and colleges have to gear up to meet this demand. One of the big challenges is having the necessary leadership within these institutions to effect the necessary changes.