英译汉,不要机译,高手来Generally, facial expression recognition system

英译汉,不要机译,高手来
Generally, facial expression recognition system consists of three steps: face detection, feature
extraction and expression classification. The same has been shown in Fig. 1. In our framework we
tracked face/salient facial regions using Viola–Jones object detection algorithm (Viola and Jones, 2001 )
as it is the most cited and considered the fastest and most accurate pattern recognition method for face
detection (Kolsch and Turk, 2004 ). The second step in the framework is feature extraction, which is the
area where this study contributes. The optimal features should minimize within- class variations of
expressions , while maximize between class variations. If inadequate features are used, even the best
classifier could fail to achieve accurate recognition (Shan et al., 2009 ). Section 3presents the novel
method for facial features extraction which is based on human visual system (HVS). To study and under- stand HVS we performed psycho-visual experiment. Psycho-visual experimental study is briefly
described in Section 4. Expression classification or recognition is the last step in the pipeline. In
literature two different ways are prevalent to recognize expressions i.e.direct recognition of prototypic
expressions or recognition of expressions through facial action coding system (FACS) action units (AUs)
(Ekman and Friesen, 1978 ). In our proposed frame- work, which is described in Section 5we directly
classify six uni- versal prototypic expressions (Ekman, 1971 ). The performance of the framework is
evaluated for five different classifiers (from different families i.e. classification tree, instance based
learning,SVM, etc.) and results are presented in Section 6. Next section presents the brief literature
review for facial features extraction methods.