人脸老化模拟与年龄估计在实际中具有广泛的应用，是人脸分析领域的重要研究方向。传统的人脸老化模拟与年龄估计算法往往依赖于一个高质量老化数据库，但这种数据库在实际中采集需要很长一段时间，且数据库在人的种族、年龄、性别上的分布有明显局限性，使得现存的人脸老化模拟及年龄估计算法难以应用于实际场景中。本项目中拟采用网络媒体中的海量数据信息，利用其中带有年龄标签的人脸视频及图像，建立多种族、年龄及性别分布较为均匀的人脸老化数据库，利用该数据库进行人脸老化模拟与年龄估计算法的研究，设计不依赖于同一个个体在不同年龄段下的人脸图像序列的人脸老化模拟算法，建立对于年龄标签中存在的噪声鲁棒的人脸年龄估计算法，并将人脸老化模拟与年龄估计算法结合为一个统一的迭代框架，提升算法的总体性能，最后将该框架应用于年龄无关的人脸识别及检索，提高在测试人脸与数据库中人脸存在年龄差距时的识别率及检索的正确率。

Human face aging simulation and age estimation has a wide range of real-world applications and is also one of the main topics in face analysis. Traditional human face aging simulation and age estimation algorithms always require a high-quality face aging database. In practice, however, the construction of this kind of database takes a very long period of time. Moreover, most currently used face aging databases has obvious limitation in the distribution of age, gender and ethnic origin, which leads to the fact that human face aging simulation and age estimation algorithms can hardly be used in the real scenario. Our project will employ the huge amounts of data in the network media and collect face images and videos with age labels from it, constructing a face aging database containing face samples from varieties of ethnic origins and with uniform distributions of age and gender. Using this database, we will research on face aging simulation and age estimation algorithms, designing face aging simulation methods not depending on individuals' face image sequences at different age groups and face age estimaion methods which is robust to the noises in the age labels. Furthermore, we will combine the face aging simulation method and age estimation algorithm in an iterative framework, enhancing the overall performance of the algorithms. Finally, this framework will be utilized in the age-invariant face recognition and retrieval, improving the recognition rate and retrieval accuracy when age differences exist between the test face and faces in the database.