Currently, there is a few numbers of color-depth (RGB-D) facial micro expressions recognition databases existing. Facial expression recognition is an important field in image processing, which has multiple uses from animation to psychology. Kinect sensor version 2 is capable of acquiring color and depth data simultaneously. Depending on the depth sensor technology, it is possible to acquire range data differently. Depth images are used in calculating range and distance between objects and the sensor. But in the last decade, another aspect of image type has emerged, named “depth image”. Color images usually consist of three channels namely Red, Green and Blue. Image databases based on their size and resolution are mostly large. This database has application in different fields such as face recognition, age estimation and Facial Expression Recognition and Facial Micro Expressions Recognition. Using suitable databases, it is possible to validate and assess available methods in different research fields. This study presents a new color-depth based face database gathered from different genders and age ranges from Iranian subjects. Intensive experiments are conducted on the CK+ and Oulu-CASIA databases, where the experimental results demonstrate that our proposed method achieves an improved performance compared with the existing state-of-the-art hand-crafted approaches. Finally, to realize reliable expression classification, a decision-level feature fusion method based on a relative majority voting (MV) strategy is also employed. A support vector machine (SVM) with multiple kernels is applied to train three base classifiers. Then we integrate multiple features of image sequences to overcome the limitation of using one single feature descriptor. Meanwhile, the facial landmarks of the peak frame are proposed to represent geometric feature (GF) and the spatiotemporal geometric feature (ST-GF) is obtained by extending it to time dimension. Specifically, we first present a new descriptor, the improved Local Binary Pattern from Three Orthogonal Planes (I-LBP-TOP), which can extract both the static and dynamic features in changing expressions, and set Gabor’s magnitude feature (GMF) as texture information. In this paper, we propose a novel expression recognition framework to mitigate this issue. However, a key issue of facial expression recognition (FER) is how to design and fuse features from videos rapidly and thus extract representative features to improve the recognition accuracy efficaciously. Experimental results demonstrate that the proposed method can achieve superior accuracy and efficiency over some state-of-the-art approaches for face detection on the FDDB dataset, as well as in the real classroom scenario.Įmotion recognition through facial expression is regarded as one of the most effective methods to directly reflect a person’s inner emotional state for affective computing. Finally, an up-and-down cropping strategy is employed to solve the problem of large population and uneven face scale in the classroom scenario. Then, all parts involving landmarks are removed to get the simplified MTCNN model, which is combined with deep residual feature generation module to improve the detection speed while the accuracy is ensured. Firstly, a deep residual feature generation module is introduced to improve the detection accuracy of small-scale faces by utilizing the characteristics of low-level fine granularity and converting the original poor features into high-resolution deformation features. Aiming at the problem of poor face detection performance under the classroom scenario with different angle of views, occlusion, and uneven distribution of face scales, a novel classroom face detection method based on the improved multi-task cascaded convolutional neural network (MTCNN) algorithm is proposed in this paper.
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