基于独立组件的模糊人脸图像鉴别

Independent Components Based Blur Face Identification

针对图像模糊会影响人脸图像识别精度的问题,首先指出了在摄取用于识别的人脸图像时加入模糊鉴别步骤的必要性,进而提出了基于独立面部组件进行模糊人脸图像鉴别的方法.由于进行模糊鉴别必须依赖图像中的高频细节信息,而人脸图像上绝大多数高频信息都集中在眼睛、眉毛、嘴巴等具体面部组件上,因此选择以这些面部组件为基本特征提取单位,以降低面颊、额头等主要包含低频平滑信息的面部其他区域对模糊鉴别精度的影响.该方法采用面部组件上的高频DCT系数为特征;随后为各组件构建独立的随机森林分类器,并分别判断每个面部组件其是否模糊;最后基于各组件的鉴别结果进行投票,得出最终模糊鉴别结果.在FRGC公开数据集上进行大量对比实验的结果表明,独立面部组件特征是有效的,并充分验证了文中方法的实际效果.

To deal with the image blur problem during face recognition ,in this paper ,the necessity to add a blur identification step ahead of facial image acquiring for recognition is first discussed ,and then a components based blur identification approach is proposed .The most significant characteristics for blur identification reside in the high frequency informations of image ,and as to a specific facial image , those informations mainly distribute on face components such as eyes ,brow ,mouth ,and so on .T hus we explore to extract features from these face components to exclude the disturbance of other face parts such as cheek and forehead w hose dominant informations are contained in low frequency .Specifically , our algorithm relies on the high frequency DCT coefficients on face component as features ,then the Random Forest strategy is utilized as the component level identifier to blur ,and finally component voting in enrolled to determine the final decision .The effectiveness of the proposed component features and our independent components based blur face identification approach are demonstrated by tremendous experiments on the publicly available FRGC dataset .