... Feature Points Location 特征点定位 Facial Feature Points Location 特征点定位 facial feature location 脸特征定位 ...
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面部特征点定位模块 SeetaFace Alignment
The main discussion of the dissertation is listed as follows:1) The history and status of research on facial feature points extraction are systematically summarized.
论文的主要工作如下:1)系统的综述了人脸特征点定位的发展历史和研究现状。
参考来源 - 人脸特征点定位研究及应用Although local feature analysis(LFA) can solve that problem, the efficiency would decline to some extent when the location of feature points was not very accurate.
局部特征分析(LFA)可以提取人脸图像的局部特征,但由于人脸特征点定位不准确通常会导致系统性能下降。
参考来源 - 基于Gabor局部特征鉴别分析的人脸识别算法AAM has been regarded as a kind of effective human facial feature points locating methods because it has good 3D expansibility, good locating effect and fast speed which reaches 230 frames per second.
由于具有三维扩展性好、特征点定位准确以及最高可达230帧/秒的处理速度等优点,AAM被认为是人脸特征点定位方法中一种有效的方法。
参考来源 - 基于AAM的人脸特征点定位方法研究·2,447,543篇论文数据，部分数据来源于NoteExpress
AAM人脸特征点定位模块是关键模块，其定位结果直接影响人脸表情动画合成效果。
AAM facial feature points positioning module is the master key for its orientation result directly influence the synthetic effect of the facial expression animation.
一般来说，人脸识别系统包括人脸检测、特征点定位、图像预处理、人脸特征提取以及人脸识别。
Generally speaking, the face recognition system consists of face detection. feature piont location, image pre-processing, feature extraction and face recognition.
然而，用现有的人脸特征点定位算法进行人脸形状估计时，嘴巴区域特征点的定位误差相对较大。
However, when estimating facial shapes using current facial landmarks detecting methods, the locating error of feature points around the mouth region is relatively large.
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