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A facial expression database is a collection of images or video clips with facial expressions of a range of emotions. Well-annotated ( emotion -tagged) media content of facial behavior is essential for training, testing, and validation of algorithms for the development of expression recognition systems .
Images, text Classification, face recognition 2011 [91] [92] M. Grgic et al. Yale Face Database Faces of 15 individuals in 11 different expressions. Labels of expressions. 165 Images Face recognition 1997 [93] [94] J. Yang et al. Cohn-Kanade AU-Coded Expression Database Large database of images with labels for expressions.
Photorealistic retinal images and vessel segmentations. Public domain. 2500 images with 1500*1152 pixels useful for segmentation and classification of veins and arteries on a single background. 2500 Images Classification, Segmentation 2020 [261] C. Valenti et al. EEG Database Study to examine EEG correlates of genetic predisposition to alcoholism.
Studierfenster (StudierFenster) is a free, non-commercial Open Science client/server-based Medical Imaging Processing (MIP) online framework. [52] Medical open network for AI is a framework for Deep learning in healthcare imaging that is open-source available under the Apache Licence and supported by the community. [53]
The Facial Recognition Technology (FERET) database is a dataset used for facial recognition system evaluation as part of the Face Recognition Technology (FERET) program.It was first established in 1993 under a collaborative effort between Harry Wechsler at George Mason University and Jonathon Phillips at the Army Research Laboratory in Adelphi, Maryland.
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As a result of the FERET program, researchers were able to establish a common baseline for comparing different face-recognition algorithms and create a large standard database of facial images that is open for research. [1] In 2003, DARPA released a high-resolution, 24-bit color version of the images in the FERET database (existing reference).
The ImageNet project is a large visual database designed for use in visual object recognition software research. More than 14 million [1] [2] images have been hand-annotated by the project to indicate what objects are pictured and in at least one million of the images, bounding boxes are also provided. [3]