Development of a robust multi-scale featured local binary pattern for improved facial expression recognition

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

Toward a flexible facial analysis framework in OpenISS for visual effects

Facial analysis, including tasks such as face detection, facial landmark detection, and facial expression recognition, is a significant research domain in computer vision for visual effects. It can be used in various domains such as facial feature mapping for movie animation, biometrics/face recognition for security systems, and driver fatigue monitoring for transportation safety assistance. Most applications involve basic face and landmark detection as preliminary analysis approaches before proceeding into further specialized processing applications. As technology develops, there are plenty of implementations and resources for each task available for researchers, but the key missing properties among them all are fexibility and usability. The integration of functionality components involves complex configurations for each connection joint which is typically problematic with poor reusability and adjustability. The lack of support for integrating different functionality components greatly impact the research effort and cost for individual researchers, which also leads us to the idea of providing a framework solution that can help regarding the issue once and for all. To address this problem, we propose a user-friendly and highly expandable facial analysis framework solution. It contains a core that supports fundamental services for the framework, and a facial analysis module composed of implementations for facial analysis tasks. We evaluate our framework solution and achieve our goals of instantiating the facial analysis specialized framework, which essentially perform tasks in face detection, facial landmark detection, and facial expression recognition. This framework solution as a whole, solves the industry problem of lacking an execution platform for integrated facial analysis implementations and fills the gap in visual effects industry