Abstract
Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional local binary pattern (LBP) is the loss of neighboring pixels related to different scales that can affect the texture of facial images. To overcome such limitations, this study describes a new extended LBP method to extract feature vectors from images, detecting each image from facial expressions. The proposed method is based on the bitwise AND operation of two rotational kernels applied on LBP(8,1) and LBP(8,2) and utilizes two accessible datasets. Firstly, the facial parts are detected and the essential components of a face are observed, such as eyes, nose, and lips. The portion of the face is then cropped to reduce the dimensions and an unsharp masking kernel is applied to sharpen the image. The filtered images then go through the feature extraction method and wait for the classification process. Four machine learning classifiers were used to verify the proposed method. This study shows that the proposed multi-scale featured local binary pattern (MSFLBP), together with Support Vector Machine (SVM), outperformed the recent LBP-based state-of-the-art approaches resulting in an accuracy of 99.12% for the Extended Cohn–Kanade (CK+) dataset and 89.08% for the Karolinska Directed Emotional Faces (KDEF) dataset.
Highlights
Facial expression recognition (FER) is a regular and incredible sign to decipher the state of human feelings and expectations, expressing human emotion without saying anything, as faces are considerably more than key to singular personalities
As it is related to human emotion, which differs from one to another, researchers discovered many methods by both machine learning and deep learning techniques to obtain a critical understanding of this matter
The given datasets are the most widely used for facial expression recognition, and this includes seven different facial expression most widely used for facial expression recognition, and this includes seven different facial expression labels or classes
Summary
Facial expression recognition (FER) is a regular and incredible sign to decipher the state of human feelings and expectations, expressing human emotion without saying anything, as faces are considerably more than key to singular personalities. One can say that it is one of the most natural, current, and robust means for communicating people’s intentions and emotions with others. As it is related to human emotion, which differs from one to another, researchers discovered many methods by both machine learning and deep learning techniques to obtain a critical understanding of this matter. Studies on FER show high demand in computer vision, which can be utilized in autonomy, neuro-advertising, scholastics, and altogether in security. FER is one of the most challenging biometric recognition technologies due to its characteristics of nature, intuition, etc
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