Name
Abstract | ||
|---|---|---|
As a severe emotional disorder, depression seriously affects people’s thoughts, behavior, feeling, sense of well-being and daily life. With the increasing number of depression patients, it has aroused the attention of researchers in this field. An effective and reliable machine learning based system has been expected to facilitate automated depression diagnose. This paper presents a novel deep transformation learning (DTL) method for visual-based depression recognition. Different from most existing depression recognition methods, our DTL trains a deep neural network that learns a set of hierarchical nonlinear transformations to project original input features into a new feature subspace, so as to capture the non-linear manifold of depression data. Extensive experiments are conducted on the AVEC2014 dataset and the results demonstrate that our method is highly competitive to several state-of-the-art methods for automated prediction of the severity of depression. |
Year | Venue | Field |
|---|---|---|
2017 | CCBR | Computer vision,Subspace topology,Computer science,Speech recognition,Emotional disorder,Artificial intelligence,Artificial neural network,Facial analysis,Feeling |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yajun Kang | 1 | 0 | 0.34 |
| Xiao Jiang | 2 | 0 | 0.34 |
| Ye Yin | 3 | 0 | 0.34 |
| Yuanyuan Shang | 4 | 210 | 16.83 |
| Xiuzhuang Zhou | 5 | 380 | 20.26 |