Indian Journal of Science and Technology
Year: 2019, Volume: 12, Issue: 11, Pages: 1-8
Ahmed Muqdad Alnasrallah* and Hanan Ali Alrikabi
*Author for correspondence
Ahmed Muqdad Alnasrallah
University of Thiqar, Thiqar, Nasiriya, Iraq.
Email: [email protected]
Objectives: There is a lot of information available in the human face expressions that reflect ones behavior and condition. Facial expressions are the most natural means of expressing the human condition. In this study, a system was designed using MATLAB to recognize the faces, as well as to know the state of face expression such as happy, sadness, fear, anger, disgust, natural, surprise. Methods/Analysis: The system designed here will be in two basic stages: The first stage is the features extraction using one of the features extraction methods (here the Local Directional Number Pattern LDNP method will be used), to extract the features from the image which input of the system. LDNP, it is a suggested technique for extracting facial features, this technology encrypts information for the face into 8 different directions, by determining the response of the edges in the neighborhood. This method will provide more features extracted from the images, which will reflected on the increase in the accuracy of the classification. The second stage is to classify the features, which extracted from the image using classification algorithms (here will used two different algorithms, SVM and ANN). Classification algorithms will apply separately to the features to compare the results and to determine which is the most accurate in the classification accuracy. Japanese Female Images database (JAFFE) was used in this paper, this database contains seven different classes of Images and the number of images is 200 pictures representing the facial expressions of 10 women. Each image was 256 × 256 pixels. The faces were in front position, to reveal all areas containing useful information. Findings: When using the system proposed in this study, the input image will be classified and categorized according to the type of facial expression, which carried it, so that the images are classified into seven different sets of facial expressions (happy, sadness, fear, anger, disgust, natural, surprise). The system evaluation process is based on the values of the following parameters (Precision, Recall, Time and Accuracy). Improvement: Two different algorithms (SVM, ANN) used to classify the attributes extracted from the image using LDNP. According to the values of the parameters above, LDNP with ANN has been shown to be the best way to detect and distinguish facial expressions.
Keywords: ANN, Face Detection and Recognition, Facial Expression, LDNP, Machine Learning, SVM.
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