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This study presents Anthropological Facial Approximation in Three Dimensions (AFA3D), a new computerized method for estimating face shape based on computed tomography (CT) scans of 500 French individuals. Facial soft tissue depths are estimated based on age, sex, corpulence, and craniometrics, and projected using reference planes to obtain the global facial appearance. Position and shape of the eyes, nose, mouth, and ears are inferred from cranial landmarks through geometric morphometrics. The 100 estimated cutaneous landmarks are then used to warp a generic face to the target facial approximation. A validation by re-sampling on a subsample demonstrated an average accuracy of c. 4 mm for the overall face. The resulting approximation is an objective probable facial shape, but is also synthetic (i.e., without texture), and therefore needs to be enhanced artistically prior to its use in forensic cases. AFA3D, integrated in the TIVMI software, is available freely for further testing.
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http://dx.doi.org/10.1111/1556-4029.12547 | DOI Listing |
Sensors (Basel)
August 2025
Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, China.
The development of deep learning-based 3D face recognition has been constrained by the limited availability of large-scale 3D facial datasets, which are costly and labor-intensive to acquire. To address this challenge, we propose a novel 2D-aided framework that reconstructs 3D face geometries from abundant 2D images, enabling scalable and cost-effective data augmentation for 3D face recognition. Our pipeline integrates 3D face reconstruction with normal component image encoding and fine-tunes a deep face recognition model to learn discriminative representations from synthetic 3D data.
View Article and Find Full Text PDFInt J Legal Med
August 2025
School of History, Classics& Archaeology, University of Edinburgh, William Robertson Wing, Teviot Place, Edinburgh, EH8 9AG, UK.
Adv Sci (Weinh)
August 2025
Behavioral and Cognitive Neuroscience Center, Institute of Science and Technology for Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Department of Endocrinology, Huadong Hospita
Despite advances in linking mouse facial expressions to emotional states, the specific facial features and neural signatures remain elusive. An artificial intelligence (AI)-based framework that decodes mouse facial expressions is presented, revealing stable valence and arousal dimensions analogous to those described in human emotion models. Facial expressions emerge as robust indicators of positive and negative emotional responses, validated through pharmacological manipulations, while responses to hallucinogens highlight the potential of valence-specific prototype modeling for interpreting previously uncharacterized emotional states.
View Article and Find Full Text PDFSensors (Basel)
July 2025
College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China.
Sheep face recognition technology is critical in key areas such as individual sheep identification and behavior monitoring. Existing sheep face recognition models typically require high computational resources. When these models are deployed on mobile or embedded devices, problems such as reduced model recognition accuracy and increased recognition time arise.
View Article and Find Full Text PDFJMIR Form Res
August 2025
Information Systems, Faculty of Computers and Information, Mansoura University, Mansoura, Egypt.
Background: Gelotophobia, the fear of being laughed at, is a social anxiety condition that affects approximately 6% of neurotypical individuals and up to 45% of those with autism spectrum disorder (ASD). This comorbidity can significantly impair the quality of life, particularly in adolescents with high-functioning ASD, where the prevalence reaches 41.98%.
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