20160601-17黄琛:Deeply Learned Rich Coding for Cross-Dataset Facial Age Estimati ...

报告嘉宾2：黄琛（香港中文大学）

报告时间：2016年6月1日（星期三）晚21:00（北京时间）

报告题目：Deeply Learned Rich Coding for Cross-Dataset Facial Age Estimation

主持人：  邓伟洪（北京邮电大学）

报告摘要：We propose a method for leveraging publicly available labeled facial age datasets to estimate age from unconstrained face images at the ChaLearn Looking at People (LAP) challenge 2015. We first learn discriminative age related representation on multiple publicly available age datasets using deep Convolutional Neural Networks (CNN). Training CNN is supervised by rich binary codes, and thus modeled as a multi-label classification problem. The codes represent different age group partitions at multiple granularities, and also gender information. We then train a regressor from deep representation to age on the small training dataset provided by LAP organizer by fusing random forest and quadratic regression with local adjustment. Finally, we evaluate the proposed method on the provided testing data. It obtains the performance of 0.287, and ranks the 3rd place in the challenge. The experimental results demonstrate that the proposed deep representation is insensitive to cross-dataset bias, and thus generalizable to new datasets collected from other sources.

参考文献：

[1] Zhanghui Kuang, Chen Huang, Wei Zhang, Deeply Learned Rich Coding for Cross-Dataset Facial Age Estimation The IEEE International Conference on Computer Vision (ICCV) Workshops, 2015, pp. 96-101

报告人简介：Chen Huang received the B.S. degree from the University of Electronic Science and Technology of China, Chengdu, in 2008, and the Ph.D. degree from Tsinghua University, Beijing, China, in 2014. He is currently a postdoctoral fellow in the Department of Information Engineering of the Chinese University of Hong Kong. He received the Best Student Paper Award at SPIE DRR XVII, San Jose, USA, in 2010. His research interests include image processing, computer vision and pattern recognition, with focus on face analysis and recognition.