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Facial expression recognition, classification 2006 [112] Binghamton University: Face Recognition Grand Challenge Dataset Up to 22 samples for each subject. Expressions: anger, happiness, sadness, surprise, disgust, puffy. 3D Data. None. 4007 Images, text Face recognition, classification 2004 [113] [114] National Institute of Standards and ...
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.
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 .
The Face Recognition Grand Challenge (FRGC) was a project that aimed to promote and advance face recognition technology to support existing face recognition efforts within the U.S. Government. The project ran from May 2004 to March 2006 and was open to face recognition researchers and developers in companies, academia, and research institutions.
FaceNet is a facial recognition system developed by Florian Schroff, Dmitry Kalenichenko and James Philbina, a group of researchers affiliated with Google.The system was first presented at the 2015 IEEE Conference on Computer Vision and Pattern Recognition. [1]
For further details check the project's GitHub repository or the Hugging Face dataset cards (taskmaster-1, taskmaster-2, taskmaster-3). Dialog/Instruction prompted 2019 [339] Byrne and Krishnamoorthi et al. DrRepair A labeled dataset for program repair. Pre-processed data Check format details in the project's worksheet. Dialog/Instruction prompted
The Caltech 101 data set was used to train and test several computer vision recognition and classification algorithms. The first paper to use Caltech 101 was an incremental Bayesian approach to one-shot learning, [ 4 ] an attempt to classify an object using only a few examples, by building on prior knowledge of other classes.
The origin of facial recognition technology is largely attributed to Woodrow Wilson Bledsoe and his work in the 1960s, when he developed a system to identify faces from a database of thousands of photographs. [6] The FERET program first began as a way to unify a large body of face-recognition technology research under a standard database.