摘要
手工设计的特征(如Gabor、LBP等)在表情识别中得到了广泛的应用。独立子空间分析是一种无监督特征学习方法,可从图像中学习出具有相位不变的特征。在表情识别应用中,由于复杂背景的影响以及人脸对齐方法的局限性,很难得到精确对齐的人脸图像序列。研究了在非精确对齐情况下,基于独立子空间分析的表情识别问题。通过分析不同子空间尺寸下的表情识别效果发现,在非精确对齐情况下,选择合适的子空间尺寸能提升学到的特征对表情识别的鲁棒性。
Hand-designed features(such as Gabor, LBP) has been widely employed in facial expression recognition. In the real-world applications of facial expression recognition, it is very difficult to achieve perfect face alignment because of the impact of complex background and the limitations of face alignment approaches. Independent Subspace Analysis(ISA) is an unsupervised feature learning method, which can be used to learn phase-invariant visual features from images. The problem of facial expression recognition based on ISA in the situation of not precise face alignment was investigated. Through analyzing the facial expression recognition performances with different subspace size, it was turned out that choosing an appropriate subspace size is important to improve the robustness of learned features for facial expression recognition in the situation of not precise alignment.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2015年第10期2316-2319 2327,2327,共5页
Journal of System Simulation
基金
福建省自然科学基金项目(2014J01246)
虚拟现实技术与系统国家重点实验室开放基金(BUAA-VR-14KF-01)
2014年安徽省科学技术厅重大科技专项项目(1301021018)
关键词
表情识别
独立子空间分析
无监督特征学习
时空特征学习
facial expression recognition
independent subspace analysis
unsupervised feature learning
spatial-temporal feature learning
