Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/234719
Title: Facial Age Estimation With Age Difference
Authors: Zhenzhen Hu;Yonggang Wen;Jianfeng Wang;Meng Wang;Richang Hong;Shuicheng Yan
Year: 2017
Publisher: IEEE
Abstract: Age estimation based on the human face remains a significant problem in computer vision and pattern recognition. In order to estimate an accurate age or age group of a facial image, most of the existing algorithms require a huge face data set attached with age labels. This imposes a constraint on the utilization of the immensely unlabeled or weakly labeled training data, e.g., the huge amount of human photos in the social networks. These images may provide no age label, but it is easy to derive the age difference for an image pair of the same person. To improve the age estimation accuracy, we propose a novel learning scheme to take advantage of these weakly labeled data through the deep convolutional neural networks. For each image pair, Kullback&x2013;Leibler divergence is employed to embed the age difference information. The entropy loss and the cross entropy loss are adaptively applied on each image to make the distribution exhibit a single peak value. The combination of these losses is designed to drive the neural network to understand the age gradually from only the age difference information. We also contribute a data set, including more than 100 000 face images attached with their taken dates. Each image is both labeled with the timestamp and people identity. Experimental results on two aging face databases show the advantages of the proposed age difference learning system, and the state-of-the-art performance is gained.
URI: http://localhost/handle/Hannan/234719
volume: 26
issue: 7
More Information: 3087,
3097
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7762921.pdf2.64 MBAdobe PDF
Title: Facial Age Estimation With Age Difference
Authors: Zhenzhen Hu;Yonggang Wen;Jianfeng Wang;Meng Wang;Richang Hong;Shuicheng Yan
Year: 2017
Publisher: IEEE
Abstract: Age estimation based on the human face remains a significant problem in computer vision and pattern recognition. In order to estimate an accurate age or age group of a facial image, most of the existing algorithms require a huge face data set attached with age labels. This imposes a constraint on the utilization of the immensely unlabeled or weakly labeled training data, e.g., the huge amount of human photos in the social networks. These images may provide no age label, but it is easy to derive the age difference for an image pair of the same person. To improve the age estimation accuracy, we propose a novel learning scheme to take advantage of these weakly labeled data through the deep convolutional neural networks. For each image pair, Kullback&x2013;Leibler divergence is employed to embed the age difference information. The entropy loss and the cross entropy loss are adaptively applied on each image to make the distribution exhibit a single peak value. The combination of these losses is designed to drive the neural network to understand the age gradually from only the age difference information. We also contribute a data set, including more than 100 000 face images attached with their taken dates. Each image is both labeled with the timestamp and people identity. Experimental results on two aging face databases show the advantages of the proposed age difference learning system, and the state-of-the-art performance is gained.
URI: http://localhost/handle/Hannan/234719
volume: 26
issue: 7
More Information: 3087,
3097
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7762921.pdf2.64 MBAdobe PDF
Title: Facial Age Estimation With Age Difference
Authors: Zhenzhen Hu;Yonggang Wen;Jianfeng Wang;Meng Wang;Richang Hong;Shuicheng Yan
Year: 2017
Publisher: IEEE
Abstract: Age estimation based on the human face remains a significant problem in computer vision and pattern recognition. In order to estimate an accurate age or age group of a facial image, most of the existing algorithms require a huge face data set attached with age labels. This imposes a constraint on the utilization of the immensely unlabeled or weakly labeled training data, e.g., the huge amount of human photos in the social networks. These images may provide no age label, but it is easy to derive the age difference for an image pair of the same person. To improve the age estimation accuracy, we propose a novel learning scheme to take advantage of these weakly labeled data through the deep convolutional neural networks. For each image pair, Kullback&x2013;Leibler divergence is employed to embed the age difference information. The entropy loss and the cross entropy loss are adaptively applied on each image to make the distribution exhibit a single peak value. The combination of these losses is designed to drive the neural network to understand the age gradually from only the age difference information. We also contribute a data set, including more than 100 000 face images attached with their taken dates. Each image is both labeled with the timestamp and people identity. Experimental results on two aging face databases show the advantages of the proposed age difference learning system, and the state-of-the-art performance is gained.
URI: http://localhost/handle/Hannan/234719
volume: 26
issue: 7
More Information: 3087,
3097
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7762921.pdf2.64 MBAdobe PDF