Emotion recognition from facial expressions is generally performed in three steps: face detection, features extraction and classification of expressions. The present work focuses on two points: Firstly, a new extraction method is presented based on the geometric approach. This method consists of calculating six distances in order to measure parts of the face that better describe a facial expression. Secondly, an automatic supervised learning method called decision tree is applied on two databases (JAFEE and COHEN), in order to have a facial expressions classifying system with seven possible classes (six basic emotions plus neutral); this system uses as input the six distances previously calculated (using Euclidian ,Manhattan or Minkowski distance) for each face. Our results achieved a recognition rate of 89.20% and 90.61% respectively in JAFFE and COHEN database.
The importance of emotion recognition lies in the role that emotions play in our everyday lives. Emotions have a strong relationship with our behavior. Thence, automatic emotion recognition, is to equip the machine of this human ability to analyze, and to understand the human emotional state, in order to anticipate his intentions from facial expression. In this paper, a new approach is proposed to enhance accuracy of emotion recognition from facial expression, which is based on input features deducted only from fiducial points. The proposed approach consists firstly on extracting 1176 dynamic features from image sequences that represent the proportions of euclidean distances between facial fiducial points in the first frame, and faicial fiducial points in the last frame. Secondly, a feature selection method is used to select only the most relevant features from them. Finally, the selected features are presented to a Neural Network (NN) classifier to classify facial expression input into emotion. The proposed approach has achieved an emotion recognition accuracy of 99% on the CK+ database, 84.7% on the Oulu-CASIA VIS database, and 93.8% on the JAFFE database.
Fuzzy logic has been used as a tool in structure-camphoraceous odor relationships. The data base studied included 99 molecules. The rules used to discriminate between camphor and non camphor molecules lead to 77% correct discrimination. Such rules account for the shape and the size of the molecule. Their adjustment by means of genetic algorithms led to 84% correct discrimination between camphor and non-camphor molecules. [figure: see text]. Membership function for the chosen variables.
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