2021-03

ICMAICS 2020

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.

2021-03

ICMAICS 2020

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.

2019-05

ICOASE2019

Abstract— Hand gestures are commonly used for communication between both impaired community and normal people. Sign languages stand for the human languages of deaf people. They form the most growing domain of research worldwide. A number of techniques were developed in this area lately. It is recognized by means of deducing the features involved in the use of hand gesture. As a matter of fact, various approaches, namely the vision-based, the data-glove-based, the colored-marker and the Electromyogram (EMG) approaches have been utilized by researchers to recognize the different hand gestures implemented in many different fields such as the whole approaches which can be divided into four main categories, viz. Data Collection, Image Processing, Feature Extraction, and Gesture Recognition. Only a few of those categories have been discussed in this paper to be compared between the accuracy rates by applying Artificial Neural Network (ANN) classification. This classification is based on different models and a novel method for Real-Time Hand Gesture Recognition System (RTHGRS). The latter has used one line (fifty features) extracted from black and white processed images to recognize the numbers from (1-10) in Kurdish Sign Language (KurdSL) using one hand only with accuracy 98%.

2018-10

International Conference of Reliable Information and Communication Technology

Information technologies are developed and grown all over the world. They depend on the computer system. Some techniques such as hand recognition are used for performing accurate recognition. The main goal of this research is to develop a system that analyzes specific human gestures then interpret this information by using computer system. This paper represents a comparative study between a new novel system called Real Time Hand Gesture Recognition System RTHGRS based on one line of features and other various techniques. The research has come out with 98% recognition compared to other researches in this filed.

2017-04

International Conference of Reliable Information and Communication Technology

Information technologies are developed and grown all over the world. They depend on the computer system. Some techniques such as hand recognition are used for performing accurate recognition. The main goal of this research is to develop a system that analyzes specific human gestures then interpret this information by using computer system. This paper represents a comparative study between a new novel system called Real Time Hand Gesture Recognition System RTHGRS based on one line of features and other various techniques. The research has come out with 98% recognition compared to other researches in this filed.