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Furthermore, the deep sparse autoencoders (DSAE) are established to recognize the facial expressions with high accuracy by learning robust and discriminative features from the data.
The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB) with effective Gabor features to recognize the facial expressions.
The paper by Lee et al., entitled "Facial affect recognition using regularized discriminant analysis-based algorithms," proposes a regularized discriminant analysis-based boosting algorithm (RDAB) with effective Gabor features to recognize the facial expressions, such as happiness, disgust, fear, anger, sadness, surprise, and neutral state.
Raccoons are assumed to recognize the facial expression and posture of other members of their species more quickly because of the conspicuous facial coloration and the alternating light and dark rings on the tail.
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These new features are used to train a support vector machine classifier that is used for recognizing the facial expressions.
Thus, when it comes to recognizing the facial expressions of others, what we think we see seems to be more important than what we actually see.
Marinkovic et al. (2000) found that surgically removing the right anterior inferior pre-frontal cortex for intractable epilepsy produced a profound deficit in recognizing the facial expression of fear, although the recognition of other facial expressions appeared to be intact.
The gender effect we found is in keeping with other studies, which showed that males are more accurate than females in recognizing the facial expression of anger [ 15, 33, 34].
Berretti et al.[13] used SIFT features detected from the depth image to recognize the seven facial expressions.
Multi-class one-against-one SVM classifier was employed to recognize the seven facial expressions; neutral, happy, sad, angry, fear, disgust, and surprise.
Learning to recognize the association between facial expressions and certain types of emotion goes a long way in being able to read emotion.
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