shape regression
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Author(s):  
Kent Nagumo ◽  
Tomohiro Kobayashi ◽  
Kosuke Oiwa ◽  
Akio Nozawa

The evaluation of physiological and psychological states using thermal infrared images is based on the skin temperature of specific regions of interest, such as the nose, mouth, and cheeks. To extract the skin temperature of the region of interest, face alignment in thermal infrared images is necessary. To date, the Active Appearance Model (AAM) has been used for face alignment in thermal infrared images. However, computation using this method is costly, and it has a low real-time performance. Conversely, face alignment of visible images using Cascaded Shape Regression (CSR) has been reported to have high real-time performance. However, no studies have been reported on face alignment in thermal infrared images using CSR. Therefore, the objective of this study was to verify the speed and robustness of face alignment in thermal infrared images using CSR. The results suggest that face alignment using CSR is more robust and computationally faster than AAM.



2020 ◽  
Vol 65 ◽  
pp. 101783
Author(s):  
Clara M. Tam ◽  
Dong Zhang ◽  
Bo Chen ◽  
Terry Peters ◽  
Shuo Li
Keyword(s):  


2020 ◽  
Vol 216 ◽  
pp. 115467 ◽  
Author(s):  
Tim Haas ◽  
Christian Schubert ◽  
Moritz Eickhoff ◽  
Herbert Pfeifer


2020 ◽  
Vol 24 (3) ◽  
pp. 825-834
Author(s):  
Hakim Christiaan Achterberg ◽  
Johan J. de Rooi ◽  
Meike W. Vernooij ◽  
M. Arfan Ikram ◽  
Wiro J. Niessen ◽  
...  


Author(s):  
Xiaohu Shao ◽  
Jiangjing Lyu ◽  
Junliang Xing ◽  
Lijun Zhang ◽  
Xiaobo Li ◽  
...  
Keyword(s):  
3D Face ◽  


2019 ◽  
Vol 28 (9) ◽  
pp. 4526-4540
Author(s):  
Hongwen Zhang ◽  
Qi Li ◽  
Zhenan Sun


Author(s):  
Chuhui Xue ◽  
Shijian Lu ◽  
Wei Zhang

State-of-the-art scene text detection techniques predict quadrilateral boxes that are prone to localization errors while dealing with straight or curved text lines of different orientations and lengths in scenes. This paper presents a novel multi-scale shape regression network (MSR) that is capable of locating text lines of different lengths, shapes and curvatures in scenes. The proposed MSR detects scene texts by predicting dense text boundary points that inherently capture the location and shape of text lines accurately and are also more tolerant to the variation of text line length as compared with the state of the arts using proposals or segmentation. Additionally, the multi-scale network extracts and fuses features at different scales which demonstrates superb tolerance to the text scale variation. Extensive experiments over several public datasets show that the proposed MSR obtains superior detection performance for both curved and straight text lines of different lengths and orientations.



2019 ◽  
Vol 41 (5) ◽  
pp. 1271-1278 ◽  
Author(s):  
Zhen Cui ◽  
Shengtao Xiao ◽  
Zhiheng Niu ◽  
Shuicheng Yan ◽  
Wenming Zheng
Keyword(s):  


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 16584-16593 ◽  
Author(s):  
Jinyu Cong ◽  
Yuanjie Zheng ◽  
Wufeng Xue ◽  
Bofeng Cao ◽  
Shuo Li


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