AIMC Topic: Automated Facial Recognition

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Learning Sequential Variation Information for Dynamic Facial Expression Recognition.

IEEE transactions on neural networks and learning systems
A multiscale sequence information fusion (MSSIF) method is presented for dynamic facial expression recognition (DFER) in video sequences. It exploits multiscale information by integrating features from individual frames, subsequences, and entire sequ...

A deep learning lightweight model for real-time captive macaque facial recognition based on an improved YOLOX model.

Zoological research
Automated behavior monitoring of macaques offers transformative potential for advancing biomedical research and animal welfare. However, reliably identifying individual macaques in group environments remains a significant challenge. This study introd...

Exploring artificial intelligence from a clinical perspective: A comparison and application analysis of two facial age predictors trained on a large-scale Chinese cosmetic patient database.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: Age prediction powered by artificial intelligence (AI) can be used as an objective technique to assess the cosmetic effect of rejuvenation surgery. Existing age-estimation models are trained on public datasets with the Caucasian race as t...

Transfer learning for mobile real-time face mask detection and localization.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Due to the COVID-19 pandemic, our daily habits have suddenly changed. Gatherings are forbidden and, even when it is possible to leave the home for health or work reasons, it is necessary to wear a face mask to reduce the possibility of con...

Convolutional Neural Network Models for Automatic Preoperative Severity Assessment in Unilateral Cleft Lip.

Plastic and reconstructive surgery
BACKGROUND: Despite the wide range of cleft lip morphology, consistent scales to categorize preoperative severity do not exist. Machine learning has been used to increase accuracy and efficiency in detection and rating of multiple conditions, yet it ...