2021-05

Journal of Physics: Conference Series

In emotion studies, critiques of the use of a static facial expression have been directed to its resulting from poor ecological validity. We conducted a study of studies in the present work, which specifically contrasted recognizing emotions using dynamic facial expressions. Brain imaging experiments and behavioural studies with associated physiological research are also included. The facial motion appears to be connected to our emotional process. The findings of laboratory brain injury experiments also reinforce the concept of a neurological dissociation between static and dynamic expression mechanisms. According to the findings of electromyography studies of dynamic expressions of affective signals, those expressions evoke more extreme facial mimic physiological responses. Studies significantly affirm the essence of dynamic facial gestures.

2021-02

Journal of Physics: Conference Series

Face expression recognition technology is one of the most recently developed fields in machine learning and has profoundly helped its users through forensic, security, and biometric applications. Many researchers and program developers have allocated their time and energy to figure out various techniques which would add to the technology's functionality and accuracy. Face expression recognition is a complicated computational process in which is implemented via analyzing changes in facial traits that follow different emotional reactions. This paper endeavors to inspect accuracy ratio of six classifiers based on Relief-F feature selection method, relying on the utilization of the minimum quantity of attributes. The classifiers in which the paper attempts to inspect are Multi-Layer Perceptron, Random Forest, Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Radial Basis Function. The experiment illustrates that K-Nearest Neighbor is the most accurate classifier with the total accuracy ratio of 94.93% amongst the rest when applied on CK+ Dataset.

2019-08

2019 2nd International Conference on Engineering Technology and its Applications

In the contemporary rapidly developing world, machine learning and pattern recognition has achieved a special level of attention by the academicians and international researchers. Such methods have economized time and increased the speed of processing tons of data in a single click. Pattern recognition is being used in a broad context, especially in industry, finance and banking, forensic issues, etc. Even though pattern selection programs have economized our time in an eye-catching way, yet choosing the best and most accurate algorithm is still a challenge for many researchers. In this paper, a Double Feature Selection (DFS) method as a new trend, using Correlation and ReliefF, is being elaborated in order to appraise the ratio of digit recognition accuracy, instead of using them individually. For this purpose, a comparison is implied to illustrate the accuracy of the four algorithms; Decision Tree (J48), KNN, Multi-Layer Perceptron (MLP), and Naïve Bayes (NB) as far as they are concerned with the digits and numerals used in Kurdish Language. The result of the study illustrates that DT has the best accuracy with the ratio of 95.6%.

2019-05

ICOASE2019

Handwritten digits recognition is considered as a core to a diversity of emerging application. It is used widely by computer vision and machine learning researchers for performing practical applications such as computerized bank check numbers reading. However, executing a computerized system to carry out certain types of duties is not easy and it is a challenging matter. Recognizing the numeral handwriting of a person from another is a hard task because each individual has a unique handwriting way. The selection of the classifiers and the number of features play a vast role in achieving best possible accuracy of classification. This paper presents a comparison of three classification algorithms namely Naive Bayes (NB), Multilayer Perceptron (MLP) and K_Star algorithm based on correlation features selection (CFS) using NIST handwritten dataset. The objective of this comparison is to find out the best classifier among the three ones that can give an acceptable accuracy rate using a minimum number of selected features. The accuracy measurement parameters are used to assess the performance of each classifier individually, which are precision, recall and F-measure. The results show that K_Star algorithm gives better recognition rate than NB and MLPas it reached the accuracy of 82.36%.

2019-05

ICOASE2019

Facial Expression Recognition (FER) has been an active topic of papers that were researched during 1990s till now, according to its importance, FER has achieved an extremely role in image processing area. FER typically performed in three stages include, face detection, feature extraction and classification. This paper presents an automatic system of face expression recognition which is able to recognize all eight basic facial expressions which are (normal, happy, angry, contempt, surprise, sad, fear and disgust) while many FER systems were proposed for recognizing only some of face expressions. For validating the method, the Extended Cohn-Kanade (CK+) dataset is used. The presented method uses Viola-Jones algorithm for face detection. Histogram of Oriented Gradients (HOG) is used as a descriptor for feature extraction from the images of expressive faces. Principal Component Analysis (PCA) applied to reduce dimensionality of the Features, to obtaining the most significant features. Finally, the presented method used three different classifiers which are Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Multilayer Perceptron Neural Network (MLPNN) for classifying the facial expressions and the results of them are compared. The experimental results show that the presented method provides the recognition rate with 93.53% when using SVM classifier, 82.97% when using MLP classifier and 79.97% when using KNN classifier which refers that the presented method provides better results while using SVM as a classifier.

2019-05

ICOASE2019

Handwritten digits recognition is considered as a core to a diversity of emerging application. It is used widely by computer vision and machine learning researchers for performing practical applications such as computerized bank check numbers reading. However, executing a computerized system to carry out certain types of duties is not easy and it is a challenging matter. Recognizing the numeral handwriting of a person from another is a hard task because each individual has a unique handwriting way. The selection of the classifiers and the number of features play a vast role in achieving best possible accuracy of classification. This paper presents a comparison of three classification algorithms namely Naive Bayes (NB), Multilayer Perceptron (MLP) and K_Star algorithm based on correlation features selection (CFS) using NIST handwritten dataset. The objective of this comparison is to find out the best classifier among the three ones that can give an acceptable accuracy rate using a minimum number of selected features. The accuracy measurement parameters are used to assess the performance of each classifier individually, which are precision, recall and F-measure. The results show that K_Star algorithm gives better recognition rate than NB and MLPas it reached the accuracy of 82.36%.