Group-aware deep feature learning for facial age estimation

Highlights：

• We propose a group-aware deep feature learning (GA-DFL) method under the deep convolutional neural networks framework. With the learned nonlinear filters, the chronological age information can be well exploited.

• We propose an overlapped coupled learning method to achieve the smoothness for the neighboring age groups. With this learning strategy, the age difference information on the age-group specific overlaps can be well measured.

• We employ a multi-path deep CNN architecture to integrate multi-scale facial information into the learned face presentation to further improve the estimation performance.

• Compared with most state-of-the-arts, experimental results show that our proposed methods have obtain significant performance on three released face aging datasets.

摘要

Highlights•We propose a group-aware deep feature learning (GA-DFL) method under the deep convolutional neural networks framework. With the learned nonlinear filters, the chronological age information can be well exploited.•We propose an overlapped coupled learning method to achieve the smoothness for the neighboring age groups. With this learning strategy, the age difference information on the age-group specific overlaps can be well measured.•We employ a multi-path deep CNN architecture to integrate multi-scale facial information into the learned face presentation to further improve the estimation performance.•Compared with most state-of-the-arts, experimental results show that our proposed methods have obtain significant performance on three released face aging datasets.

论文评审过程：Received 13 July 2016, Revised 29 September 2016, Accepted 20 October 2016, Available online 26 October 2016, Version of Record 12 March 2017.