This article is dedicated to analyze various facial expression recognition method based on different type of image, which aim at extracting feature on the image. Considering the method to optimize existing method

Cheng, Chengliang

Zhang, Hengrui

Repository of Belarusian National Technical University (BNTU)

237   Список использованных источников 1. Шульга, К. В. Применение PID регулирования для управления судомоделью / К. В. Шульга, С. И. Билалов, Д. В. Деменковец // Компьютерные системы и сети: 57-я научная конференция аспирантов, магистрантов и студентов, Минск, 19–23 апреля 2021 г. : сборник тезисов докладов / Белорусский государственный университет информатики и радиоэлектро-ники. – Минск, 2021. – С. 65–66. 2. Шульга, К. В. Применение математической модели PID-регулятора для симуляции движения судомодели = Application of a mathematical model of the PID controller to simulate the motion of a ship model / К. В. Шульга, Д. В. Деменковец // Компьютерные системы и сети : сборник статей 58-й научной конференции аспирантов, магистрантов и студентов, Минск, 18–22 апреля 2022 г. / Белорусский государственный университет информатики и радиоэлектро-ники. – Минск, 2022. – С. 52–56. 3. Бутрим, А. И. Программно-аппаратный модуль управления судомоделью на базе МК STM32 / А. И. Бутрим, Д. В. Деменковец // Компьютерные системы и сети : сборник статей 58-й научной конференции аспирантов, магистрантов и студентов, Минск, 18–22 апреля 2022 г. / Белорусский государственный университет информатики и радиоэлектроники. – Минск, 2022. – С. 108–110.  УДК 004.8 FACIAL EXPRESSION RECOGNITION METHOD ON STATIC AND DYNAMIC IMAGE Cheng Chengliang, Zhang Hengrui Belarusian State University of Informatics and Radioelectronics e-mail: cclstrawberry@gmail.com  Summary. This article is dedicated to analyze various facial expression recognition method based on different type of image, which aim at extracting feature on the image. Considering the method to optimize existing method.  Expression is a word that is mentioned a lot in our daily life. In interpersonal communication, people can enhance their communication effect by controlling their facial expressions. Facial expres-sions are an important way to spread human emotional information and coordinate interpersonal rela-tionships. According to research, in human’s daily communication, the information transmitted through language only accounts for 7 % of the total information, while the information transmitted through facial expressions reaches 55 % of the total information. Expression is an indicator of emotion projected by humans and other animals from their physical appearance, mostly referring to the state formed by facial muscles and five senses, such as smiling, angry eyes, etc. It also includes the body language expressed by the body as a whole. Some expressions can be interpreted accurately, even among mem-bers of different species, with anger and extreme satisfaction being prime examples. However, some expressions are difficult to interpret, even among familiar individuals, with disgust and fear being the main examples. In general, the various organs of the face are an organic whole, expressing the same emotion in a coordinated manner. Facial expressions are part of the human physical language, a physi-ological as well as psychological response, and are usually used to convey emotions. The face expression recognition system mainly consists of four parts: face image acquisition, face detection, feature extraction, and feature classification. The face expression recognition system has developed into an independent research direction due to the fact that the open source expression database is now relatively abundant, the image acquisition is not too difficult, and the face detection algorithm is relatively mature, so the research on face expression recognition is mainly reflected in the last two steps of the system: feature extraction and feature classification.  Emotion feature extraction mainly uses mathematical methods and relies on computer tech-238   nology to organize and process data from digital images of human face expressions, extract expres-sion features and remove non-expression noise. In some cases, the feature extraction algorithm ex-tracts the main features of the image and objectively reduces the dimensionality of the image. The generation of facial expressions is a complex process, and if psychological and environmental factors are not taken into account, what is presented to the observer is simply muscle movement and the resulting changes in facial shape and texture. The static image presents the expression state of a single image when the expression occurs, and the dynamic image presents the movement process of the expression between multiple images. Therefore, the expression feature extraction algorithms are broadly divided into static image based feature extraction methods and dynamic image based feature extraction methods according to the state of expression occurrence and the processing object.  Feature extraction methods based on static images: 1. Integral method.  Face expressions rely on muscle movements to reflect. The static image of face expression visually shows the changes of facial shape and texture produced by the movement of face muscles when the expression occurs. From a holistic perspective, such changes cause significant deformation of facial organs, which can have an impact on the global information of the face image, and therefore face expression recognition algorithms that consider expression features from a holistic perspective have emerged.  2. Partial method.  Face expressions on static images not only have overall variations, but also local variations exist. The information contained in the local deformation of facial muscles such as texture and wrin-kles can help determine the attributes of expressions accurately. The classical methods of local method are Gabor wavelet method and LBP operator method.  Dynamic image based feature extraction method: The difference between dynamic images and static images is that dynamic images reflect the process of facial expression occurrence. Therefore, the expression features of dynamic images are mainly expressed in the continuous deformation of the face and the muscle movement of different regions of the face. The current feature extraction methods based on dynamic images are mainly divided into optical flow method, model method and geometric method.  1. Optical flow method. Optical flow method is a method to reflect the grayscale changes of corresponding objects between different frames in dynamic images. Firstly, the optical flow field and gradient field between consecutive frames are used to represent the spatial and temporal changes of the image respectively to achieve the tracking of expression regions in each frame of face image; then, the movement of face muscles is represented by the change of motion direction of feature regions, which then corresponds to different expressions.  2. Model method. The model method in face expression recognition refers to the statistical method to paramet-rically describe the expression information of dynamic images. Commonly used algorithms mainly include Active Shape Model Method (ASM) and Active Appearance Model Method (AAM), both of which can be divided into two parts: shape model and subjective model. As far as the appearance model is concerned, ASM reflects the local texture information of the image, while AAM reflects the global texture information of the image.  3. Geometric method.  Marking feature points in the facial organ regions and calculating the distance between feature points and the curvature of the curve where the feature points are located becomes a method to extract facial expressions using geometric forms. The faces with different expressions are represented on a grid using a deformation grid, and the change of grid node coordinates between the first frame and the largest frame of that sequence of expressions is used as a geometric feature to realize the recog-nition of expressions. 

Facial expression recognition method on static and dynamic image

https://rep.bntu.by/bitstream/data/125628/1/237-238.pdf

Facial expression recognition method on static and dynamic image

Abstract

Similar works

Full text

Available Versions

Repository of Belarusian National Technical University (BNTU)