Due to numerous difficulties, including the variation in face shapes between individuals, the challenge of recognizing dynamic facial attributes, the poor quality of digital images, etc., detecting human emotion depending on facial expression is difficult for the computer vision community. Thus, in this study, we propose an approach for emotion recognition depending on facial expression using histogram of oriented gradients and convolution neural network (HOG-CNN). The HOG-CNN composed of three stages, median filter, HOG, and CNN. The first stage is preprocessing using median filter. The second stage is feature extraction using HOG. The third stage is classification using CNN. The proposed method was tested and evaluated on the UMD face database. The system attained a high performance with a mean average accuracy of 98.07%, average precision of 94.78%, and average recall of 97.15%.
This survey paper describes a number of fields in which human behavior analysis through facial gestures is applied, the various method that is utilized with the most recent technologies. Cutting-edge camera technology was used to capture images and assess a person's emotions. Human behavior is studied through facial and body gestures, and in this study, we divide human emotions into universally acknowledged expressions such as "sad," "happy," "surprised," "worried," and "liar." The limitations and benefits of competing and complementary technologies are discussed in this study, as well as the diversity of research in the area of human behavioral analysis based on face and gestures.
After the development in the field of visual data analysis, it has become necessary to obtain objects inside the image, which must be with a high degree of accuracy and clarity for the purpose of identifying the object inside that body in order to obtain clear living organisms, as well as medical professionals can clarify the types of diseases and their forms to find Treatments suitable for her.In this paper was used a set of previously used and proven mechanisms and techniques that help to remove impurities from the images and reveal the edges to distinguish the object's boundaries and the length of the body and calculate the center of gravity and through the mechanism of the body's rotation to identify these things so that specialists can determine what these things are and how to deal with them And develop possible solutions to it, whether medical, geographical or otherwise.
It has been relied upon and is still found in the fields of scientific research, especially astronomy, medicine (for accurate disease diagnosis), biology, archeology, and industry on video and still images. The low accuracy and quality of some videos are often due to a poor lens type or angle, which may be due to a lack of photographic experience, or because of the older sections, which can be affected by the coolness of the surrounding perimeter. This research was completed using simple methods of processing based on using a program to convert video to individual images, then a number of image processing operations to improve quality, and finally re-assemble the images to the video more accurately than the original and in our own way. The proposed process consists of several steps: cutting the video clip into a set of images, performing various operations, such as using the contrast filter first, discovering the edges, smoothing the image, and improving image density prior to assembly. We finally assemble the images back into clips. This has been the process we used on many of the films affected by noise, or damaged for a long time, and has proven our ability to improve the quality of the video.
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