Automatically detecting facial expressions has become an important research area. It plays a significant role in security, human-computer interaction and health-care. Yet, earlier work focuses on posed facial expression. In this paper, we propose a spontaneous facial expression recognition method based on effective feature extraction and facial expression recognition for Micro Expression analysis. In feature extraction we used histogram of oriented gradients (HOG) descriptor to extract facial expression features. Expression recognition is performed by using a Support vector machine (SVM) classifier to recognize six emotions (happiness, anger, disgust, fear, sadness and surprise). Experiments show promising results of the proposed method with recognition accuracy of 95% on static images while 80% on videos.
The goal of our paper is to obtain superior accuracy of different classifiers or multi-classifiers fusion in diagnosing Hepatitis using world wide data set from Ljubljana University. We present an implementation among some of the classification methods which are defined as the best algorithms in medical field. Then we apply a fusion between classifiers to get the best multiclassifier fusion approach. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. The experimental results show that for all data sets (complete, reduced, and no missing value) using multi-classifiers fusion ac hi e ve d be t t e r accuracy than the single ones.
Weighted average voting has been widely used in control and computing systems. A novel voting algorithm based on combining the weighted average voter (WAV) with the minimum distance classification (MDC) technique. This voting algorithm uses WAV to indicate the minimum distance that will be used in the classification technique to select one measurement as the voter output. It is applicable for handling the output of an array of skewed sensors in safety related applications. The performance of the proposed voter is evaluated through a series of fault injection experiments and the results are compared with those of the exact majority voter [1], the WAV [2] and the enhanced weighted average voter (EWAV) [3]. The experimental results showed that the novel voter gives better performance in terms of reliability and complexity than the three other voters.
The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
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