摘要
为了更准确地识别人的表情,在识别人脸7种基本表情(愤怒、厌恶、恐惧、高兴、无表情、悲伤和惊讶)时,采用了局域二值模式技术提取面部特征,进行由粗略到精细的表情分类。在粗略分类阶段,7种基本表情中的2种表情被选为初步分类结果(候选表情)。在精细分类阶段,选用计算加权卡方值确定最终分类结果。采用日本的Jaffe表情数据库来验证算法性能,对陌生人表情的识别率为77.9%,其结果优于采用同样数据库的其他方法,且易于实现。
This paper presents an effective method for facial expression recognition, It analyzes seven basic expressions： angry, disgust, fear, happiness, neutral, sadness and surprise. The local binary pattern（LBP） operator is used to extract face appearance features. A two-stage classification method is proposed. At the first （coarse classification） stage, two expression candidates from initial seven are selected. At the second （fine classification） stage, one of the two candidate classes is verified as final expression class by the Chi square statistic. Our algorithm is tested on the Jaffe database and the recognition rate for a new person＇s expression is 77.9%, which is higher than all the other reported methods.
出处
《计算机工程》
CAS
CSCD
北大核心
2007年第5期168-169,172,共3页
Computer Engineering
基金
芬兰CIMO基金资助项目
国家留学基金资助项目
西北工业大学"英才计划"基金资助项目
关键词
面部表情
局域二值模式
表情分类
加权卡方值
Facial expression
Local binary pattern（LBP）
Expression classification
Chi square statistic
