Abstract: Aiming at the problem of poor face recognition and low recognition rate in complex environment,the algorithm of extracting facial features by sparse learning and significance theory is proposed in this paper.Through the sparse coding theory to simulate the human visual perception mechanism,constructing multi-scale multi-directional filter to extract image contour feature by using the basis function,the feature is subjected to Local Binary Pattern(LBP) filtering to highlight the face local detail texture feature.According to the visual attention mechanism,the salient features of the treated features are constructed and the contribution of important features to face recognition is enhanced.It uses the LFW,YALE standard library and home-made video frame image library for testing to obtain a higher recognition rate.Experimental results show that the proposed method is superior to the traditional feature extraction method.The obtained facial features are more representative and it has strong robustness in the complex environment.

Key words: complex environment, feature extraction, sparse coding, visual attention mechanism, saliency map

摘要： 针对复杂环境下人脸识别效果差、识别率低等问题,提出一种利用稀疏学习及显著性理论提取人脸特征的方法。基于稀疏编码理论模拟人类视觉感知机制,利用学习得到的基函数构造多尺度多方向滤波器提取图像外观轮廓特征,并对该特征做局部二值模式滤波,以突出人脸局部细节纹理特征。依据视觉注意机制对处理后的特征构造显著图,增强关键特征对于人脸识别的贡献。在LFW、YALE标准库和自制视频帧图像库上的实验结果表明,该方法的识别率高于传统特征提取方法,得到的人脸特征更具代表性,并且在复杂环境下具有较强的鲁棒性。

关键词: 复杂环境, 特征提取, 稀疏编码, 视觉注意机制, 显著图

CLC Number: 

• TP391.4