Abstract
Human emotion is highly correlated to facial expressions. Due to its growing demand in different sectors, an emotion recognition method is proposed through recognizing facial expressions. The input image is preprocessed and then the resulting image is segmented into four facial expression regions following the newly proposed segmentation method. Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are fused to extract the necessary features from the four segmented parts. The dimension of the feature vector is reduced using Principal Component Analysis (PCA). To classify the expressions, Extreme Learning Machine (ELM) is used. For evaluating the performance of the proposed method, three widely used and publicly available facial expression datasets (JAFFE, CK+, RaFD) are used. The proposed method achieved 95.3%, 99.84% and 98.65% accuracy while using images from JAFFE, CK+ and RaFD dataset respectively. Performance of the proposed method on these datasets is compared to other facial expression recognition methods on these datasets to indicate that the proposed method achieves state-of-the-art performance.
Highlights
Ability to detect the mental condition, feelings of a person can be of great importance
Successful recognition of human emotion, feelings is highly dependent on the successful recognition of facial expressions
Recognized facial expressions can be used in different sectors of our lives for improving the everyday experience
Summary
Ability to detect the mental condition, feelings of a person can be of great importance. Recognized facial expressions can be used in different sectors of our lives for improving the everyday experience It can be used for security purposes as well. Seven basic facial expressions are usually considered while dealing with Facial Expression Recognition (FER) problems. They are neutral, fear, disgust, sad, happy, angry and surprise (Sandbach et al, 2012). Some works are done excluding neutral expressions As it has many applications in many sectors, researchers have been trying to develop FER systems with the ability to recognize expressions accurately in the least possible time. FER systems include image preprocessing, feature extraction and classification step. To handle these problems, the proposed method uses ELM to classify the expressions to their corresponding classes. Last few sections analyze the performance of the proposed method, compare performance with other methods and discuss the flaw, future possibilities of the work
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