Analysis of Markerless-Based Tracking Methods of Face Tracker Techniques in Detecting Human Face Movements in 2D And 3D Filter Making

M.Ilham Arief, Kusrini Kusrini, Tonny Hidayat
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Abstract

The marker-based tracking method is a method that utilizes markers, while the markerless-based tracking method is a method that does not use markers in making AR. In the markerless-based tracking method, there is a face tracker technique. In previous research, no one has discussed the comparison of effectiveness concerning the success and accuracy of using the face tracker technique. Therefore, this study aims to test the effectiveness of the accuracy and accuracy of success with applying the markerless-based tracking method, the face tracker technique, in detecting facial movements. in 2D and 3D AR with light intensity test parameters of 20 Lux, 40 Lux, and 60 Lux with WRGB light color, Face angle position of 30o and 60o, and face distance from camera 50 cm, 100cm, and 150cm. The results of comparison of superior success accuracy are at a distance of 50 cm; with an accuracy rate for 2D AR of 93.22% and 96.63% for 3D. It was concluded that the face tracker technique's markerless-based tracking method works optimally in 3D compared to 2D. This research finds an attractiveness score of 1.865, a perception score of 1.683, an efficiency score of 1.550, a dependability score of 1.638, a stimulation score of 1.500, and a novelty score of 1.013. Quality with an attractiveness value of 1.68, pragmatic quality of 1.56, and hedonic quality of 1.26. This study concludes that 2D and 3D AR face detection positively evaluates user experience and quality.    
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基于无标记跟踪的人脸跟踪技术在二维和三维滤波器制作中检测人脸运动的方法分析
基于标记的跟踪方法是一种利用标记的方法,而基于无标记的跟踪方法是一种不使用标记进行AR的方法。在基于无标记的跟踪方法中,有一种面部跟踪技术。在以往的研究中,没有人讨论过使用人脸跟踪技术的成功率和准确性的有效性比较。因此,本研究旨在通过应用基于无标记的跟踪方法,即人脸跟踪技术,来检测人脸运动的准确性和成功准确率的有效性。在2D和3D AR中,光照强度测试参数分别为20lux、40lux和60lux,使用WRGB光色,面部角度位置分别为30o和60o,面部距离相机50cm、100cm和150cm。在50 cm距离处,比较了较优的成功精度;2D AR的准确率为93.22%,3D AR的准确率为96.63%。结果表明,基于无标记的人脸跟踪技术在三维环境下的跟踪效果优于二维环境。本研究发现,吸引力得分为1.865,感知得分为1.683,效率得分为1.550,可靠性得分为1.638,刺激得分为1.500,新奇得分为1.013。质量的吸引力值为1.68,实用质量为1.56,享乐质量为1.26。本研究得出2D和3D AR人脸检测对用户体验和质量有积极的评价。
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审稿时长
10 weeks
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