A FACE DETECTION METHOD USING HOG AND MTCNN ALGORITHMS
This paper researches and evaluates two methods of face detection, namely the HOG method representing the approach of using the facial features and the MTCNN method representing the use of deep learning and neural networks. To evaluate these two methods, the theory of each method was first presented. After that, the experimental model is used on the hardware platform, which is an embedded Jetson TX2 computer with an advanced GPU graphics processing chip. The face angle parameter was used to evaluate the detection level and accuracy for each method. In addition, the experimental model also evaluates the speed of face detection from the camera for each method. The results show that the average time for face detection by HOG method is 0.16 s and MTCNN method is 0.58 s. For face-to-face frames, both methods detect very well with an accuracy rate of 100 %, with face inclination angles of 30o, 60o, 90o, the MTCNN method gives more accurate results. This is also consistent with published studies. Finally, the results of this study can be used as a basis for choosing face detection methods and choosing hardware devices for recognition problems.
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