Conventional respiration measurement requires a separate device and/or can cause discomfort, so it is difficult to perform routinely, even for patients with respiratory diseases. The development of contactless respiration measurement technology would reduce discomfort and help detect and prevent fatal diseases. Therefore, we propose a respiration measurement method using a learning-based region-of-interest detector and a clustering-based respiration pixel estimation technique. The proposed method consists of a model for classifying whether a pixel conveys respiration information based on its variance and a method for classifying pixels with clear breathing components using the symmetry of the respiration signals. The proposed method was evaluated with the data of 14 men and women acquired in an actual environment, and it was confirmed that the average error was within approximately 0.1 bpm. In addition, a Bland–Altman analysis confirmed that the measurement result had no error bias, and regression analysis confirmed that the correlation of the results with the reference is high. The proposed method, designed to be inexpensive, fast, and robust to noise, is potentially suitable for practical use in clinical scenarios.

The biometric technique of iris recognition is considerably limited by the cost of optical devices and user inconvenience. Periocular-based methods are an alternative means of biometric authentication because they do not require expensive equipment. Moreover, the resulting data are suitable for biometrics because they include features such as eyelashes, eyebrows, and eyelids. However, conventional periocular-based biometric authentication methods use limited sets of features that are dependent on the selected feature extraction method, resulting in relatively poor performance. Therefore, we propose a deep-learning-based method that actively utilizes the various features contained in periocular images. The method maintains the mid-level features of the convolutional layers and selectively utilizes features useful for classification. We compared the proposed method with previous methods using public and self-collected databases. The experimental results show that the equal error rate is less than 1%, which is superior to the previous methods. In addition, we propose a new method to analyze whether mid-stage features have been utilized. As a result, it was confirmed that this approach, which utilizes the mid-level features, can effectively improve the feature extraction performance of the network. INDEX TERMS Convolutional neural networks, deep learning, mid-level features, periocular biometrics, periocular recognition.

Human respiration reflects meaningful information, such as one’s health and psychological state. Rates of respiration are an important indicator in medicine because they are directly related to life, death, and the onset of a serious disease. In this study, we propose a noncontact method to measure respiration. Our proposed approach uses a standard RGB camera and does not require any special equipment. Measurement is performed automatically by detecting body landmarks to identify regions of interest (RoIs). We adopt a learning model trained to measure motion and respiration by analyzing movement from RoI images for high robustness to background noise. We collected a remote respiration measurement dataset to train the proposed method and compared its measurement performance with that of representative existing methods. Experimentally, the proposed method showed a performance similar to that of existing methods in a stable environment with restricted motion. However, its performance was significantly improved compared to existing methods owing to its robustness to motion noise. In an environment with partial occlusion and small body movement, the error of the existing methods was 4–8 bpm, whereas the error of our proposed method was around 0.1 bpm. In addition, by measuring the time required to perform each step of the respiration measurement process, we confirmed that the proposed method can be implemented in real time at over 30 FPS using only a standard CPU. Since the proposed approach shows state-of-the-art accuracy with the error of 0.1 bpm in the wild, it can be expanded to various applications, such as medicine, home healthcare, emotional marketing, forensic investigation, and fitness in future research.

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