A Concatenational Graph Evolution Aging Model
A Concatenational Graph Evolution Aging Model
复制标题
DOI：
10.1109/tpami.2012.22
复制
发表时间：
2012-11
影响因子：
23.6
通讯作者：
Jin-Li Suo;Xilin Chen;S. Shan;Wen Gao;Qionghai Dai
中科院分区：
文献类型：
--
作者：
Jin-Li Suo;Xilin Chen;S. Shan;Wen Gao;Qionghai Dai
人脸长期老化过程的建模对于人脸识别和动画制作具有重要意义，但缺乏足够的人脸长期老化序列用于模型学习。为了解决这个问题，我们提出了一个级联GRaph进化（CONGRE）老化模型，它采用在空间和时间方面的分解策略，学习长期老化模式，从部分密集老化数据库。在空间方面，我们建立了一个图形化的人脸表示，其中一个人的脸被分解成相互关联的子区域解剖指导下。在时间方面，上述图形表示的长期演变，然后通过连接连续的短期模式的老化过程的马尔可夫性质下相邻的短期模式之间的平滑性约束和子区域之间的一致性约束。该模型还考虑了人脸老化的多样性，提出了短期模式之间的概率级联策略，并将学校抽样应用于老化预测。在实验中，老化预测结果的学习老化模型产生的主观和客观的评估，以验证所提出的模型。
Modeling the long-term face aging process is of great importance for face recognition and animation, but there is a lack of sufficient long-term face aging sequences for model learning. To address this problem, we propose a CONcatenational GRaph Evolution (CONGRE) aging model, which adopts decomposition strategy in both spatial and temporal aspects to learn long-term aging patterns from partially dense aging databases. In spatial aspect, we build a graphical face representation, in which a human face is decomposed into mutually interrelated subregions under anatomical guidance. In temporal aspect, the long-term evolution of the above graphical representation is then modeled by connecting sequential short-term patterns following the Markov property of aging process under smoothness constraints between neighboring short-term patterns and consistency constraints among subregions. The proposed model also considers the diversity of face aging by proposing probabilistic concatenation strategy between short-term patterns and applying scholastic sampling in aging prediction. In experiments, the aging prediction results generated by the learned aging models are evaluated both subjectively and objectively to validate the proposed model.