Description: 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-In order to more accurately identify the person s facial expressions, in the identification of seven kinds of basic facial expressions (anger, disgust, fear, happy, expressionless, sadness and surprise), the use of local binary pattern of facial features extraction techniques, carried out by the rough to the fine expression classification. In the rough classification stage, the seven kinds of basic expressions in the two kinds of expression was selected as the initial classification results (candidate expressions). In the fine classification stage, the choice of calculating the weighted chi-square value to determine the final classification results. Jaffe expressions used in Japan to validate algorithm performance database of strangers face recognition rate was 77.9, the result is better than using the same database in other ways, and are easy to achieve Platform: |
Size: 212992 |
Author:张波 |
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Description: aleix@ecn.
Semantic queries to a database of images are more
desirable than low-level feature queries, because they
facilitate the user s task. One such approach is the
object-related image retrieval. In the context of face
images, it is of interest to retrieve images based on
people s names and facial expressions. However, when
images of the database are allowed to appear at dif-
ferent facial expressions, the face recognition approach
encounters the expression-invariant problem, i.e. how
to robustly identify a person s face for which its learn- Platform: |
Size: 152576 |
Author:a v |
Hits:
Description: aleix@ecn.
Semantic queries to a database of images are more
desirable than low-level feature queries, because they
facilitate the user s task. One such approach is the
object-related image retrieval. In the context
people s names and facial expressions. However, when
images of the database are allowed to appear at dif-
ferent facial expressions, the face recognition approach
encounters the expression-invariant problem, i.e. how
to robustly identify a person s face for which its learn- Platform: |
Size: 357376 |
Author:a v |
Hits:
Description:
facilitate the user s task. One such approach is the
object-related image retrieval. In the context of face
images, it is of interest to retrieve images based on
people s names and facial expressions. However, when
images of the database are allowed to appear at dif-
ferent facial expressions, the face recognition approach
encounters the expression-invariant problem, i.e. how
to robustly identify a person s face for which its learn- Platform: |
Size: 23552 |
Author:a v |
Hits:
Description: aleix@ecn.
Semantic queries to a database of images are more
desirable than low-level feature queries, because they
facilitate the user s task. One such approach is the
object-related image retrieval. In the context of face
images, it is of interest to retrieve images based on
people s names and facial expressions. However, when
images of the database are allowed to appear at dif-
ferent facial expressions, the face recognition approach
encounters the expression-invariant problem, i.e. how
to robustly identify a person s face for which its learn- Platform: |
Size: 47104 |
Author:a v |
Hits:
Description: aleix@ecn.
Semantic queries to a database of images are more
desirable than low-level feature queries, because they
facilitate the user s task. One such approach is the
object-related image retrieval. In the context of face
images, it is of interest to retrieve images based on
people s names and facial expressions. However, when
images of the database are allowed to appear at dif-
ferent facial expressions, the face recognition approach
encounters the expression-invariant problem, i.e. how
to robustly identify a person s face for which its learn- Platform: |
Size: 80896 |
Author:a v |
Hits:
Search - person s facial expression