2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2017)
Honolulu, Hawaii, USA
July 21, 2017 to July 26, 2017
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPRW.2017.77
While very promising results have been shown on face recognition related problems, age-invariant face recognition still remains a challenge. Facial appearance of a person changes over time, which results in significant intraclass variations. In order to address this problem, we propose a novel deep face recognition network called age estimation guided convolutional neural network (AE-CNN) to separate the variations caused by aging from the personspecific features which are stable. The carefully designed CNN model can learn age-invariant features for face recognition. To the best of our knowledge, this is the first attempt to use age estimation task for obtaining age-invariant features. Extensive results on two well-known public domain face aging datasets: MORPH Album 2 and CACD show the effectiveness of the proposed approach.
Face recognition, Face, Aging, Estimation, Feature extraction
T. Zheng, W. Deng and J. Hu, "Age Estimation Guided Convolutional Neural Network for Age-Invariant Face Recognition," 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, Hawaii, USA, 2017, pp. 503-511.