多尺度局部二值模式傅里叶直方图特征的表情识别

Multi-scale local binary pattern fourier histogram features for facial expression recognition

针对表情识别的简便快捷问题,提出一种多尺度局部二值模式傅里叶直方图(LBP-HF)和主动形状模型(ASM)相结合的人脸表情识别方法。该方法首先利用ASM检测并分割人脸区域,减少不相关区域的影响;然后提取多尺度LBP-HF特征形成识别向量;最后采用最近邻分类方法进行表情识别。通过提取不同尺度的LBP-HF特征,研究各个尺度LBP-HF特征对表情识别的影响,最终结合多尺度LBP-HF特征实现表情识别,获得更有效的表情特征。通过与Gabor特征的实验结果进行对比,验证该方法的简便可行性,最高平均识别率达到93.5%。实验结果表明,该方法可以用于人机交互中。

To achieve simple and convenient facial expression recognition, a method combining muhi-scale Local Binary Pattern Histogram Fourier （LBP-HF） and Active Shape Model （ASM） was proposed. Firstly, the face regions were detected and segmented by ASM to reduce the influence of unrelated regions, and then LBP-HF were extracted to form recognition vectors. Finally, the nearest neighborhood classifier was applied to recognize expressions. The influences of various scale LBP- HF features on facial expression recognition were studied through extracting LBP-HF features from different scales. At last, muhi-scale LBP-HF features were concatenated to discriminate expressions, and more effective expression features were obtained. By comparison with the experimental result of Gabor features, its feasibility and simplieation are validated, and the highest mean recognition rate is 93.50%. The experimental results demonstrate that the method can be used for human- computer interaction.