This paper proposes a kind of face recognition algorithm based on image difference among the front face pictures with normal expression.
针对表情正常的正面人脸图像，提出了一种基于图像差值的识别算法。
Human face problems consist of four parts: human face detection, human face tracking, human face recognition and the derived analysis of pose and expression.
人脸问题主要包括：人脸检测、人脸跟踪、人脸识别，以及其衍生出来的姿态和表情分析四个应用领域。
It finds applications in various areas, such as face detection, face recognition, gesture recognition, expression recognition, face image compression and reconstruction, and face cartoon.
它可广泛应用于人脸跟踪、人脸识别、姿态识别、表情识别、头部像压缩及重构、脸部动画等领域。
This study discusses, mainly through the means of behavior experiment, the influence of the facial arrangements on the recognition of partial-face expression.
本研究主要采用行为实验探讨面孔呈现顺序对局部表情识别产生的影响。
A facial expression recognition system contains face detection, face feature extraction, feature selection and expression classification.
表情识别系统包括人脸检测、人脸特征提取、特征选择以及表情分类等几部分。
Facial feature points localization takes an important role in the face recognition, facial expression analysis, cartoon face synthesis, etc.
人脸特征点的定位在人脸识别、人脸表情分析以及卡通人脸生成等方面具有非常重要的作用。
The experiments on the ORL face database show that the recognition rate of the proposed method is high when pose, illumination condition, face expression and training sample number change.
在OR L人脸库的测试结果表明，在姿态、光照、表情、训练样本数目变化的情况下，该算法都具有较好的识别率。
However, because of the complex facial structure, the diverse facial expression and the changing light intensity, face recognition is still being recognized as a challenging research.
但人脸图像中表情、姿态、光照度等内外在因素皆多变，使得该研究至今仍颇具挑战性。
However, because of the complex facial structure, the diverse facial expression and the changing light intensity, face recognition is still being recognized as a challenging research.
但人脸图像中表情、姿态、光照度等内外在因素皆多变，使得该研究至今仍颇具挑战性。
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