As a traditional Chinese medicine practice, acupuncture has long been shown to benefit pain and stress relief (especially for elderly people with chronical cases). Therefore, acupuncture is an important and effective alternative medical therapy for disabled elderly population living in areas of low healthcare coverage, which has become a more and more serious social problem as the Chinese population ages rapidly. However, training of acupuncturists is quite expensive and time consuming. With the arrival of the era of AI, how to automate the process of acupuncture treatment and minimalize the involvement of human labor has emerged as a great challenge and opportunity. This research studies a prerequisite of automatic acupuncture treatment: patient in-position detection during the acupuncture treatment process. We propose a fast and accurate one-stage anchor-free DNN model for patient in-position detection. Our model is an improvement of the basis model, YOLO X. The proposed framework consists of a backbone of CSP-DarkNet, a neck of feature pyramid network and a Decoupled Head. As for loss function, we combine the CIoU and the alpha-IoU losses to inherit both their advantages. A simplified version of the advanced label assignment technique of OTA, as well as data augmentation strategies of Mosaic and MixUp are utilized to improve the algorithm performance. Results on a self-collected dataset of acupuncture treatment (named as ATPD Dataset) show that our algorithm significantly outperform other state-of-the-art methods in the literature that are either multiple-staged or single-staged.
Face detection is one of the most important research topics in the field of computer vision, and it is also the premise and an essential part of face recognition. With the advent of deep learning-based techniques, the performance of face detection has been largely improved and more and more daily applications have been witnessed. However, face detection is greatly affected by environmental illumination. Most of existing face detection algorithms neglect harsh illumination conditions such as nighttime condition where lighting is insufficient or it is totally dark. These conditions are often encountered in real-world scenarios, e.g., nighttime surveillance in law enforcement or civil settings. How to overcome the problem of face detection in the darkness becomes a critical and urgent demand. We thus in this paper study face detection in the darkness using infrared (IR) imaging. We build an IR face detection dataset and design a deep learning-based model to study the face detection performance. Specifically, the deep learning model is a Single Stage Detector which has the advantage of fast speed and lower computation cost compared with other face detectors that consists of multiple stages. In the experiment, we also compare the performance of our deep learning model with that of a well-known traditional face detection algorithm, AdaBoost. In terms of True Positive Rate (TPR), our model significantly outperforms AdaBoost by 5% -- a dramatic boost from 87% to 92%, which suggests our deep learning-based method with IR imaging can indeed meet the requirement of real-world nighttime face detection applications.
Recognition of individual identity using the periocular region (i.e., periocular recognition) has emerged as a relatively new modality of biometrics and is a potential substitute for face recognition when facial occlusion happens, e.g., when wearing a mask. Moreover, many application scenarios occur at nighttime, such as nighttime surveillance in law reinforcement. We therefore study the topic of periocular recognition at nighttime using the infrared spectrum. However, the useful and effective area for periocular recognition is quite limited compared to that of face recognition since only the eyes are exposed. As a result, the performance of periocular recognition algorithms is relatively low. This issue of limited area poses a serious challenge even though many state-of-the-art face recognition algorithms yield high performance. This situation is even more deteriorated when periocular recognition is performed at nighttime. Thus, we in this paper propose an image super-resolution (SR) based technique for nighttime periocular recognition in which we enlarge the small-sized periocular image to have a larger effective area while retaining a high image quality. Super-resolution of the periocular images is achieved by a CNN model which first conducts interpolation of the periocular area to an expected size and then finds a nonlinear mapping between the input low quality periocular image and the output high quality periocular image. To validate our method, we compare our deep learning-based SR method with the original case of none SR involved at all, as well as the other two cases using traditional SR methods, namely bilinear interpolation and bicubic interpolation. In terms of quality metrics such as PSNR and SSIM as well as recognition metrics such as GAR and EER, our method significantly outperforms all the other three methods.
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