Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/128094
Title: Data-Dependent Label Distribution Learning for Age Estimation
Authors: Zhouzhou He;Xi Li;Zhongfei Zhang;Fei Wu;Xin Geng;Yaqing Zhang;Ming-Hsuan Yang;Yueting Zhuang
Year: 2017
Publisher: IEEE
Abstract: As an important and challenging problem in computer vision, face age estimation is typically cast as a classification or regression problem over a set of face samples with respect to several ordinal age labels, which have intrinsically cross-age correlations across adjacent age dimensions. As a result, such correlations usually lead to the age label ambiguities of the face samples. Namely, each face sample is associated with a latent label distribution that encodes the cross-age correlation information on label ambiguities. Motivated by this observation, we propose a totally data-driven label distribution learning approach to adaptively learn the latent label distributions. The proposed approach is capable of effectively discovering the intrinsic age distribution patterns for cross-age correlation analysis on the basis of the local context structures of face samples. Without any prior assumptions on the forms of label distribution learning, our approach is able to flexibly model the sample-specific context aware label distribution properties by solving a multi-task problem, which jointly optimizes the tasks of age-label distribution learning and age prediction for individuals. Experimental results demonstrate the effectiveness of our approach.
URI: http://localhost/handle/Hannan/128094
volume: 26
issue: 8
More Information: 3846,
3858
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7822912.pdf3.2 MBAdobe PDF
Title: Data-Dependent Label Distribution Learning for Age Estimation
Authors: Zhouzhou He;Xi Li;Zhongfei Zhang;Fei Wu;Xin Geng;Yaqing Zhang;Ming-Hsuan Yang;Yueting Zhuang
Year: 2017
Publisher: IEEE
Abstract: As an important and challenging problem in computer vision, face age estimation is typically cast as a classification or regression problem over a set of face samples with respect to several ordinal age labels, which have intrinsically cross-age correlations across adjacent age dimensions. As a result, such correlations usually lead to the age label ambiguities of the face samples. Namely, each face sample is associated with a latent label distribution that encodes the cross-age correlation information on label ambiguities. Motivated by this observation, we propose a totally data-driven label distribution learning approach to adaptively learn the latent label distributions. The proposed approach is capable of effectively discovering the intrinsic age distribution patterns for cross-age correlation analysis on the basis of the local context structures of face samples. Without any prior assumptions on the forms of label distribution learning, our approach is able to flexibly model the sample-specific context aware label distribution properties by solving a multi-task problem, which jointly optimizes the tasks of age-label distribution learning and age prediction for individuals. Experimental results demonstrate the effectiveness of our approach.
URI: http://localhost/handle/Hannan/128094
volume: 26
issue: 8
More Information: 3846,
3858
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7822912.pdf3.2 MBAdobe PDF
Title: Data-Dependent Label Distribution Learning for Age Estimation
Authors: Zhouzhou He;Xi Li;Zhongfei Zhang;Fei Wu;Xin Geng;Yaqing Zhang;Ming-Hsuan Yang;Yueting Zhuang
Year: 2017
Publisher: IEEE
Abstract: As an important and challenging problem in computer vision, face age estimation is typically cast as a classification or regression problem over a set of face samples with respect to several ordinal age labels, which have intrinsically cross-age correlations across adjacent age dimensions. As a result, such correlations usually lead to the age label ambiguities of the face samples. Namely, each face sample is associated with a latent label distribution that encodes the cross-age correlation information on label ambiguities. Motivated by this observation, we propose a totally data-driven label distribution learning approach to adaptively learn the latent label distributions. The proposed approach is capable of effectively discovering the intrinsic age distribution patterns for cross-age correlation analysis on the basis of the local context structures of face samples. Without any prior assumptions on the forms of label distribution learning, our approach is able to flexibly model the sample-specific context aware label distribution properties by solving a multi-task problem, which jointly optimizes the tasks of age-label distribution learning and age prediction for individuals. Experimental results demonstrate the effectiveness of our approach.
URI: http://localhost/handle/Hannan/128094
volume: 26
issue: 8
More Information: 3846,
3858
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7822912.pdf3.2 MBAdobe PDF