Facial Emotion Identification Based on Local Binary Pattern Feature Detector

摘要

Objectives: The emotion detection is one of the important fields in computer human interaction and this study plays significant a role for identification facial expression from the images. To identify the single emotion, need a various variability of human shapes such as pose, color, texture, expression, posture and orientation. In this study, we implement Local Binary Pattern (LBP) based filters for identifying the dynamic face textures. And moreover, this approach also provides extension and simplification. Methods/Statistical Analysis: We used built-in FER2013 datasets, the database consisting seven classes (Surprise, Fear, Angry, Neutral, Sad, Disgust, Happy). The dataset is divided into three parts testing, validation and training (15% and 70%). The Convolution neural network is trained with feature Descriptor Local Binary Pattern. Findings: The experimental results have demonstrated that local LBP representations are effective in spatial dynamic feature extraction, as they encode the information of image texture configuration while providing local structure patterns. The advantages of our approach include local processing, robustness to monotonic grayscale changes and simple computation. The results show that, the performance LBP based Convolution Neural Network (CNN) model is better than conventional CNN. This research study further helps in image classification and image processing fields. Application/Improvements: It is recommended that LBP should be used for finding the local regions or pattern from the image. The LBP computation and local processing is quite better with robustness and monotonic changes.