This study introduces a novel Facial Emotion Recognition (FER) algorithm designed for uncontrolled, or "in the wild," environments. Traditional image-based FER systems face challenges in these settings due to variations in pose, occlusion, lighting, and skin tones. Our approach overcomes these limitations by extracting and utilizing 3D facial landmarks instead of relying directly on image-based features. We employ the MediaPipe FaceMesh model to extract 478 normalized facial landmarks from the FER+ dataset, which includes images labeled with eight emotions. These landmarks define 2556 face tessellations, serving as embedding features for a transformer-based network. The algorithm's unique aspect lies in its ability to normalize images across different conditions and cameras, using a consistent set of features derived from 3D landmarks. This normalization enables the integration of diverse FER datasets for training, enhancing the algorithm’s applicability across various devices. Achieving 73.7% accuracy on the FER+ dataset, the algorithm demonstrates significant promise in emotion classification. Its adaptive attention mechanism focuses on well-represented landmarks, accounting for the face's angle and aspects, rather than solely on the emotion itself. This study marks a significant advancement in FER technology, offering a robust solution for emotion recognition in real-world and varied conditions.

In this paper, we propose human perception mechanism based CNN-RNN based approach to recognize dimensional emotion using Aff-Wild dataset. The proposed model recognizes dimensional expression through two steps. First, motivated by the human facial expression perception mechanism, the patches were extracted based on facial key points. Second, the facial expression was classified based on CNN-RNN model using extracted patches. The mean concordance correlation coefficient (CCC) of the proposed model is 0.322, indicating superior performance compared to the original CNN-RNN model, which achieved a mean CCC of 0.283.

Abstract As the pandemic situation continues, many people exercise at home. Mat Pilates is a popular workout and effective core strengthening. Although many researchers have conducted pose recognition studies for exercise posture correction, the study on Pilates exercise is only one case on static images. Therefore, for the purpose of exercise monitoring, we propose a real-time Pilates posture recognition system on a smartphone for exercise monitoring. We aimed to recognize 8 Pilates exercises—Bridge, Head roll-up, Hundred, Roll-up, Teaser, Plank, Thigh stretch, and Swan. First, the Blazepose model is used to extract body joint features. Then, we designed a deep neural network model that recognizes Pilates based on the extracted body features. It also measures the number of workouts, duration, and similarity to experts in video sequences. The precision, recall, and f1-score of the posture recognition model are 0.90, 0.87, and 0.88, respectively. The introduced application is expected to be used for exercise management at home.

To limit the spread of COVID-19, thermal screening cameras were installed everywhere. These cameras observe many thermal faces. These thermal face data are generally used to monitor strange temperatures for COVID-19 screening or to maintain social distancing. Big data of Thermal face generated everywhere should be used in the more practical functions. We proposed a method to measure non-contact breathing signals using thermal face data. In addition, breathing signals data estimated from thermal face data was converted to DICOM waveform Information Object Definitions (IODs) for interoperability management of medical data. The proposed method was tested on a golden reference (chest belt) with a mean accuracy of 93.52 %. a proposed method that can extract breathing signals using thermal screening cameras that are widely available around the world and manage data as healthcare interoperability information can show important potential in the public, telemedicine field in the future.

In the past few years, the use of big data has been important for public health. However, it was difficult to use medical imaging as public data. Several problems, such as data privacy and inconsistent measurement system, were not suitable for the interoperability in big data. In this paper, we proposed the reference model that satisfies the interoperability of medical imaging data and able to be shared in public data server. The de-identification method minimizes personal information, in order to protect the confidentiality of patients. The quantification method for medical imaging was to calibrate the measured values using a reference material. Finally, de-identified and quantified medical imaging data was shared with a public server.