A New Method for Head Direction Estimation based on Dlib Face Detection Method and Implementation of Sine Invers Function
The detection and tracking of head movements have been such an active area of research during the past years. This area contributes highly to computer vision and has many applications of computer vision. Thus, several methods and algorithms of face detection have been proposed because they are requi...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | AR EN |
| Publicado: |
College of Education for Pure Sciences
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/e2d9779af3b24e5b82bc52b0e509aa2a |
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| Sumario: | The detection and tracking of head movements have been such an active area of research during the past years. This area contributes highly to computer vision and has many applications of computer vision. Thus, several methods and algorithms of face detection have been proposed because they are required in most modern applications, in which they act as the cornerstone in many interactive projects. Implementation of the detected angles of the head or head direction is very useful in many fields, such as disabled people assistance, criminal behavior tracking, and other medical applications. In this paper, a new method is proposed to estimate the angles of head direction based on Dlib face detection algorithm that predicts 68 landmarks in the human face. The calculations are mainly based on the predicated landmarks to estimate three types of angles Yaw, Pitch and Roll. A python program has been designed to perform face detection and its direction. To ensure accurate estimation, the particular landmarks were selected, such that, they are not affected by the movement of the head, so, the calculated angles are approximately accurate. The experimental results showed high accuracy measures for the entire three angles according to real and predicted measures. The sample standard deviation results for each real and calculated angle were Yaw (0.0046), Pitch (0.0077), and Roll (0.0021), which confirm the accuracy of the proposed method compared with other studies. Moreover, the method performs faster which promotes accurate online tracking. |
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