Pose Estimation of Driver’s Head Panning Based on Interpolation and Motion Vectors under a Boosting Framework

Over the last decade, a driver’s distraction has gained popularity due to its increased significance and high impact on road accidents. Various factors, such as mood disorder, anxiety, nervousness, illness, loud music, and driver’s head rotation, contribute significantly to causing a distraction. Many solutions have been proposed to address this problem; however, various aspects of it are still unresolved. The study proposes novel geometric and spatial scale-invariant features under a boosting framework for detecting a driver’s distraction due to the driver’s head panning. These features are calculated using facial landmark detection algorithms, including the Active Shape Model (ASM) and Boosted Regression with Markov Networks (BoRMaN). The proposed approach is compared with six existing state-of-the-art approaches using four benchmark datasets, including DrivFace dataset, Boston University (BU) dataset, FT-UMT dataset, and Pointing’04 dataset. The proposed approach outperforms the existing approaches achieving an accuracy of 94.43%, 92.08%, 96.63%, and 83.25% on standard datasets.

TFW: Annotated Thermal Faces in the Wild Dataset

Face detection and localization of facial landmarks are the primary steps in building many face applications in computer vision. Numerous algorithms and benchmark datasets have been proposed to develop accurate face and facial landmark detection models in the visual domain. However, varying illumination conditions still pose challenging problems. Thermal cameras can address this problem because of their operation in longer wavelengths. However, thermal face detection and localization of facial landmarks in the wild condition are overlooked. The main reason is that most of the existing thermal face datasets have been collected in controlled environments. In addition, many of them contain no annotations of face bounding boxes and facial landmarks. In this work, we present a thermal face dataset with manually labeled bounding boxes and facial landmarks to address these problems. The dataset contains 9,202 images of 145 subjects, collected in both controlled and wild conditions. As a baseline, we trained the YOLOv5 object detection model and its adaptation for face detection, YOLO5Face, on our dataset. To show the efficacy of our dataset, we evaluated these models on the RWTH-Aachen thermal face dataset in addition to our test set. We have made the dataset, source code, and pretrained models publicly available at https://github.com/IS2AI/TFW to bolster research in thermal face analysis. <br>

TFW: Annotated Thermal Faces in the Wild Dataset

Face detection and localization of facial landmarks are the primary steps in building many face applications in computer vision. Numerous algorithms and benchmark datasets have been proposed to develop accurate face and facial landmark detection models in the visual domain. However, varying illumination conditions still pose challenging problems. Thermal cameras can address this problem because of their operation in longer wavelengths. However, thermal face detection and localization of facial landmarks in the wild condition are overlooked. The main reason is that most of the existing thermal face datasets have been collected in controlled environments. In addition, many of them contain no annotations of face bounding boxes and facial landmarks. In this work, we present a thermal face dataset with manually labeled bounding boxes and facial landmarks to address these problems. The dataset contains 9,202 images of 145 subjects, collected in both controlled and wild conditions. As a baseline, we trained the YOLOv5 object detection model and its adaptation for face detection, YOLO5Face, on our dataset. To show the efficacy of our dataset, we evaluated these models on the RWTH-Aachen thermal face dataset in addition to our test set. We have made the dataset, source code, and pretrained models publicly available at https://github.com/IS2AI/TFW to bolster research in thermal face analysis. <br>

A Python toolbox for Automatic Face Alignment (AFA)

We describe AFA, an open-source Python package for automating the most common step in the preparation of facial stimuli for behavioral and neuro-imaging experiments – spatial alignment of faces (https://github.com/SourCherries/auto-face-align ). Face alignment is also important in the analysis of image statistics, and as a preprocessing step for machine learning. Automation of face alignment via AFA provides a reliable and efficient alternative to the very common practice of manual image-editing in graphics editors like Photoshop. As an open-source Python package, AFA encourages a clear and transparent specification of experimental method. AFA is based on facial landmark detection that is powered by the reliable and open-source DLIB library; and critical alignment code based on Generalized Procrustes Analysis (GPA) has been extensively unit-tested. AFA documentation and modularity provides opportunity for the modification and extensibility of AFA by the scientific community. As examples, we include functions for automatically generating image apertures that conceal areas outside the inner face; for image morphing between facial identities; and for shape-based averaging of facial identity. All of these are examples of stimulus preparation that have previously required manual landmark selection.

Adaptación y comparación de dos metodologías de reconocimiento facial aplicadas a la detección de somnolencia en conductores

Este artículo busca comparar dos metodologías de reconocimiento de expresionesfaciales: Viola-Jones y Regression Based Facial Landmark Detection, adaptados para la detec-ción de somnolencia, dando a conocer cuál de ellas es la más óptima y se adecúa mejor a lascondiciones variables, considerando las restricciones de oclusión, rotación de rostro, ilumi-nación. Tras un análisis cuantitativo bajo la matriz de confusión y poniendo a prueba lasmetodologías propuestas en diferentes situaciones, se realizó una comparación con los resul-tados obtenidos. Ocurren diferentes efectos por la falta de sueño como la disminución deltiempo de reacción, cansancio ocular, la visión se torna borrosa, menor concentración, etcé-tera; influyendo de manera directa en el incremento de accidentes de tráfico.