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 recognition has grown rapidly in the past several years due to advances in deep learning. More and more applications have emerged as this technology becomes more mature. However, face recognition under uncontrolled conditions is still quite challenging. For example, real-world applications usually encounter the issue of non-frontal standing pose which causes the face recognition system to degrade or even fail. Thus, this research work studies the issue of non-ideal facial pose in face recognition and propose to addresses this problem via pose-aware quality assessment and judgement. We first implement a standard face recognition system, consisting of an MTCNN face detection stage and a FaceNet face recognition stage. Then, we introduce a Quality Assessment and Judgement (QAJ) stage between the face detection stage and the face recognition stage. The QAJ stage conducts facial pose estimation which is realized through a DNN. Given a facial input, the QAJ stage assesses the facial pose and judges if the input is satisfactory in terms of quality. Inputs of poor quality will be screened and dropped out while inputs of high quality will be passed to the subsequent face recognition stage to output a final recognized identity. In the experiments, we compare the face recognition rates with and without the QAJ stage. Using a pose threshold of 15°, we find out that the recognition rate is improved by 2.83%, which is a significant improvement on the recognition performance and justifies our proposed technique of QAJ.
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.
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.
The periocular region is considered as a relatively new modality of biometrics and serves as a substitute solution for face recognition with occlusion. Moreover, many application scenarios occur at nighttime, such as nighttime surveillance. To address this problem, we study the topic of periocular recognition at nighttime using the infrared spectrum. Utilizing a simplified version of DeepFace, a convolutional neural networks designed for face recognition, we investigate nighttime periocular recognition at both short and long standoffs, namely 1.5 m, 50 m and 106 m. A subband of the active infrared spectrum { near-infrared (NIR) { is involved. During generation of the periocular dataset, preprocessing is conducted on the original face images, including alignment, cropping and intensity conversion. The verification results of the periocular region using DeepFace are compared with the results of two conventional methods { LBP and PCA. Experiments have shown that the DeepFace algorithm performs fairly well (with GAR over 90% at FAR=0.1%) using the periocular region as a modality even at nighttime. The framework also shows superiority to both LBP and PCA in all cases of different light wavelengths and standoffs.
Cross-spectral matching of active infrared (IR) facial probes to a visible light facial gallery is a new challenging problem. This scenario is brought up by a number of real-world surveillance tasks such as recognition of subjects at night or under severe atmospheric conditions. When combined with long distance, this problem becomes even more challenging due to deteriorated quality of the IR data, causing another issue called image quality disparity between the visible light and the IR imagery. To address this quality disparity in the heterogeneous images due to atmospheric and camera effects - typical degrading factors observed in long range IR data, we propose an image fusion-based method which fuses multiple IR facial images together and yields a higher-quality IR facial image. Wavelet decomposition using the Harr basis is conducted first and then the coefficients are merged according to a rule that treats the high and low frequencies differently, followed by an inverse wavelet transform step to reconstruct the final higher-quality IR facial image. Two sub-bands of the IR spectrum, namely short-wave infrared (SWIR) and near-infrared (NIR), as well as two different long standoffs of 50 m and 106 m are involved. Experiments show that in all cases of different sub-bands and standoffs our image fusion-based method outperforms the one without image fusion, with GARs significantly increased by 3.51% and 1.09% for SWIR 50 m and NIR 50 m at FAR=10%, respectively. The equal error rates are reduced by 2.61% and 0.90% for SWIR 50 m and NIR 50 m, respectively.
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