In the head-mounted display environment for experiencing metaverse or virtual reality, conventional input devices cannot be used, so a new type of nonintrusive and continuous biometric authentication technology is required. Since the wrist wearable device is equipped with a photoplethysmogram sensor, it is very suitable for use for nonintrusive and continuous biometric authentication purposes. In this study, we propose a one-dimensional Siamese network biometric identification model using a photoplethysmogram. To maintain the unique characteristics of each person and reduce noise in preprocessing, we adopted a multicycle averaging method without using a bandpass or low-pass filter. In addition, to verify the effectiveness of the multicycle averaging method, the number of cycles was changed and the results were compared. Genuine and impostor data were used to verify the biometric identification. We used the one-dimensional Siamese network to verify the similarity between the classes and found that the method with five overlapping cycles was the most effective. Tests were conducted on the overlapping data of five single-cycle signals and excellent identification results were observed, with an AUC score of 0.988 and an accuracy of 0.9723. Thus, the proposed biometric identification model is time-efficient and shows excellent security performance, even in devices with limited computational capabilities, such as wearable devices. Consequently, our proposed method has the following advantages compared with previous works. First, the effect of noise reduction and information preservation through multicycle averaging was experimentally verified by varying the number of photoplethysmogram cycles. Second, by analyzing authentication performance through genuine and impostor matching analysis based on a one-dimensional Siamese network, the accuracy that is not affected by the number of enrolled subjects was derived.

In colonoscopy, computer vision and artificial intelligence technology have enabled the automatic detection of the location of polyps and their visualization. These advancements have facilitated considerable research in the field. However, deep learning models used in the segmentation problem for capturing various patterns of polyps are becoming increasingly complex, which has rendered their operation in real time difficult. To identify and overcome this problem, a study was conducted on a model capable of precise polyp segmentation while increasing its processing speed. First, an efficient, high-performance, and lightweight model suitable for the segmentation of polyps was sought; the performance of existing segmentation models was compared and combined to obtain a learning model that exhibited good accuracy and speed. Next, hyperparameters were found for the MobileNetV3-encoder-based DeepLabV3+ model and, after tuning the hyperparameters, quantitative and qualitative results were compared, and the final model was selected. The experimental results showed that this model achieved high accuracy, with a Dice coefficient of 93.79%, while using a limited number of parameters and computational resources. Specifically, the model used 6.18 million parameters and 1.623 giga floating point operations for the CVC-ClinicDB dataset. This study revealed that increasing the amount of computation and parameters of the model did not guarantee unconditional performance. Furthermore, for the search and removal of polyps in cases in which morphological information is critical, an efficient model with low model complexity and high accuracy was proposed for real-time segmentation.

This study introduces AffectiVR, a dataset designed for periocular biometric authentication and emotion evaluation in virtual reality (VR) environments. To maximize immersion in VR environments, interactions must be seamless and natural, with unobtrusive authentication and emotion recognition technologies playing a crucial role. This study proposes a method for user authentication by utilizing periocular images captured by a camera attached to a VR headset. Existing datasets have lacked periocular images acquired in VR environments, limiting their practical application. To address this, periocular images were collected from 100 participants using the HTC Vive Pro and Pupil Labs infrared cameras in a VR environment. Participants also watched seven emotion-inducing videos, and emotional evaluations for each video were conducted. The final dataset comprises 1988 monocular videos and corresponding self-assessment manikin (SAM) evaluations for each experimental video. This study also presents a baseline study to evaluate the performance of biometric authentication using the collected dataset. A deep learning model was used to analyze the performance of biometric authentication based on periocular data collected in a VR environment, confirming the potential for implicit and continuous authentication. The high-resolution periocular images collected in this study provide valuable data not only for user authentication but also for emotion evaluation research. The dataset developed in this study can be used to enhance user immersion in VR environments and as a foundational resource for advancing emotion recognition and authentication technologies in fields such as education, therapy, and entertainment. This dataset offers new research opportunities for non-invasive continuous authentication and emotion recognition in VR environments, and it is expected to significantly contribute to the future development of related technologies.

The blood oxygen saturation, which indicates the ratio of oxygenated hemoglobin to total hemoglobin in the blood, is closely related to one’s health status. Oxygen saturation is typically measured using a pulse oximeter. However, this method can cause skin irritation, and in situations where there is a risk of infectious diseases, the use of such contact-based oxygen saturation measurement devices can increase the risk of infection. Therefore, recently, methods for estimating oxygen saturation using facial or hand images have been proposed. In this paper, we propose a method for estimating oxygen saturation from facial images based on a convolutional neural network (CNN). Particularly, instead of arbitrarily calculating the AC and DC components, which are essential for measuring oxygen saturation, we directly utilized signals obtained from facial images to train the model and predict oxygen saturation. Moreover, to account for the time-consuming nature of accurately measuring oxygen saturation, we diversified the model inputs. As a result, for inputs of 10 s, the Pearson correlation coefficient was calculated as 0.570, the mean absolute error was 1.755%, the root mean square error was 2.284%, and the intraclass correlation coefficient was 0.574. For inputs of 20 s, these metrics were calculated as 0.630, 1.720%, 2.219%, and 0.681, respectively. For inputs of 30 s, they were calculated as 0.663, 2.142%, 2.612%, and 0.646, respectively. This confirms that it is possible to estimate oxygen saturation without calculating the AC and DC components, which heavily influence the prediction results. Furthermore, we analyzed how the trained model predicted oxygen saturation through ‘SHapley Additive exPlanations’ and found significant variations in the feature contributions among participants. This indicates that, for more accurate predictions of oxygen saturation, it may be necessary to individually select appropriate color channels for each participant.

This study proposes a biometric authentication method using infrared (IR)-based periocular images captured in virtual reality (VR) environments with head-mounted displays (HMDs). The widespread application of VR technology highlights the growing need for robust user authentication in immersive environments. To address this, the study introduces a novel periocular biometric authentication system optimized for HMD usage. Ensuring reliable authentication in VR environments necessitates overcoming significant challenges, including flicker noise and infrared reflection. Flicker noise, caused by alternating current (AC)-powered lighting, produces banding artifacts in images captured by rolling-shutter cameras, obstructing biometric feature extraction. Additionally, IR reflection generates strong light glare on the iris surface, degrading image quality and negatively impacting the model’s generalization performance and authentication accuracy. This study utilized the AffectiVR dataset, which includes noisy images, to address these challenges. In the preprocessing phase, iris reflections were removed, reducing the Equal Error Rate (EER) from 6.73% to 5.52%. Furthermore, incorporating a Squeeze-and-Excitation (SE) block to mitigate flicker noise and enhance model robustness resulted in a final EER of 6.39%. Although the SE block slightly increased the EER, it significantly improved the model’s ability to suppress noise and focus on critical periocular features, ensuring enhanced robustness in challenging VR environments. Heatmap analysis revealed that the proposed model effectively utilized periocular features, such as the skin around the eyes and eye contours, compared to prior approaches. This study establishes a crucial groundwork for advancing robust biometric authentication systems capable of overcoming noise challenges in next-generation immersive platforms.