This study examined whether our ability to accurately estimate unfamiliar faces' ages declines when they are wearing sunglasses or surgical-style face masks and whether these disguises make it harder to later recognise those faces when undisguised. In theory, both disguises should harm age estimation accuracy and later face recognition as they occlude facial information that is used to determine a face's age and identity. To establish whether this is the case, we had participants estimate the age of unfamiliar faces that were pictured wearing no disguises, sunglasses, or face masks. The participants then completed a face recognition test where they had to distinguish between the previously seen faces and new faces. Importantly, none of faces wore disguises in this latter test. Participants' estimates of the undisguised faces' ages were inaccurate by a Median of 5.15 years. Their accuracy barely changed when the faces wore sunglasses but declined by a Median of 1.30 years when they wore face masks. Moreover, subsequent undisguised face recognition was less likely to occur when the faces previously wore sunglasses or face masks, with large effects observed. These findings demonstrate the relative importance of different facial areas when estimating faces' ages and later recognising them. They also have implications for policing as they suggest it may be harder for eyewitnesses to accurately estimate the age of criminals who wear face masks during offences, and it may be harder for them to later recognise criminals in line-ups if the criminals wear sunglasses or face masks during offences.
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http://dx.doi.org/10.1186/s41235-022-00370-0 | DOI Listing |
Comput Biol Med
January 2025
School of Information Science and Engineering, Yunnan University, 650500, Kunming, China. Electronic address:
In the treatment of brain tumors, accurate diagnosis and treatment heavily rely on reliable brain tumor segmentation, where multimodal Magnetic Resonance Imaging (MRI) plays a pivotal role by providing valuable complementary information. This integration significantly enhances the performance of brain tumor segmentation. However, due to the uneven grayscale distribution, irregular shapes, and significant size variations in brain tumor images, this task remains highly challenging.
View Article and Find Full Text PDFAbdom Radiol (NY)
January 2025
Department of Radiology, Shenzhen Traditional Chinese Medicine Hospital (The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine), Shenzhen, China.
Purpose: Intra-pancreatic fat deposition (IPFD) is closely associated with the onset and progression of type 2 diabetes mellitus (T2DM). We aimed to develop an accurate and automated method for assessing IPFD on multi-echo Dixon MRI.
Materials And Methods: In this retrospective study, 534 patients from two centers who underwent upper abdomen MRI and completed multi-echo and double-echo Dixon MRI were included.
MDM Policy Pract
January 2025
Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA.
Unlabelled: COVID-19 tremendously disrupted the global health system. People of all ages were at risk of becoming infected. Frequent school closures raised concerns about both the physical and mental health of school-age children.
View Article and Find Full Text PDFInfluenza Other Respir Viruses
January 2025
Nivel - Netherlands Institute for Health Services Research, Utrecht, The Netherlands.
Background: During the COVID-19 pandemic, atypical respiratory syncytial virus (RSV) circulation patterns emerged, with the occurrence of RSV activity outside the typical winter season. This study investigates the impact of COVID-19 and associated non-pharmaceutical interventions (NPIs) on RSV seasonality.
Methods: The onset, offset and peak of RSV epidemics from 2018 to 2022 across 12 European countries were determined using the 3% positivity threshold method.
Med Image Anal
January 2025
School of Engineering Medicine and School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China; Key Laboratory of Big DataBased Precision Medicine (Beihang University), Ministry of Industry and Information Technology of China, Beijing, 100191, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; National Key Laboratory of Kidney Diseases, Beijing, 100853, China. Electronic address:
Precise cerebrovascular segmentation in Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) data is crucial for computer-aided clinical diagnosis. The sparse distribution of cerebrovascular structures within TOF-MRA images often results in high costs for manual data labeling. Leveraging unlabeled TOF-MRA data can significantly enhance model performance.
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