Partial Occlusion Face Recognition Method Based on Acupoints locating through Infrared Thermal Imaging

Lian, Yangyang; Wang, Zhihui; Yuan, Hao; Gao, Lifang; Yu, Zhuozhi; Chen, Wenwei; Xing, Yifei; Xu, Siya; Feng, Lei

doi:10.1109/iwcmc48107.2020.9148064

Cited by 2 publications

(1 citation statement)

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“…The conventional method to locate acupoints on human body requires the massagists to have rich experience and skillful performance, and the learning cost is always high [12][13][14][15][16]. The visual positioning technology of massage acupoints based on image registration can lower the technical difficulty, thereby allowing more people to enjoy and benefit from massage therapy, thus it is of utterly importance to research the visual positioning of massage acupoints based on image registration [17][18][19][20][21], and this research can spur technical advancement in fields such as computer vision, image processing, and biomedical engineering, enlightening new sparkles in research ideas and methods.…”

Section: Introductionmentioning

confidence: 99%

A Novel Visual Positioning Algorithm for Massage Acupoints Based on Image Registration

Zhang¹,

Yu²,

Hu³

et al. 2023

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The conventional method to locate acupuncture points (acupoints) on human body requires the massagists to have rich experience and skillful performance, and the learning cost is always high. The visual positioning technology of massage acupoints based on image registration can lower the technical difficulty, thereby allowing more people to enjoy and benefit from massage therapy. However, existing algorithms for this technology generally have a series of shortcomings including the unstable matching results, the inaccurate image registration effect, and the unsatisfactory results in case of obvious local deformation or occlusion. In view of these matters, this paper studied a novel visual positioning algorithm for acupoints based on image registration. At first, an Image Acupoints Positioning algorithm was proposed based on Convolution Neural Network (CNN-based IAP algorithm), the algorithm can combine the prior information of acupoint positions in visual images with 3D CNN, which has a stronger feature expression ability, and maintain high positioning accuracy under unfavorable conditions such as image noise, illumination change, or occlusion. Then, based on the structure of Fully Convolutional Network (FCN), a multi-scale parallel FCN was constructed, which has introduced the techniques of multiscale parallel downsampling, spatial pyramid of dilated convolutions, adaptive channel attention mechanism, direction perception, and upsampling, intending to improve the model's performance in non-rigid registration of the visual images of massage acupoints. At last, the validity of the proposed model was verified by experimental results.

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