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Face recognition, classification 2011 [111] Zhao, G. et al. BU-3DFE neutral face, and 6 expressions: anger, happiness, sadness, surprise, disgust, fear (4 levels). 3D images extracted. None. 2500 Images, text Facial expression recognition, classification 2006 [112] Binghamton University: Face Recognition Grand Challenge Dataset
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 .
FACE Challenges – recognition of individuals from photographs posted on social media. [9] Face in Video Evaluation (FIVE) – ability of algorithms to identify or ignore persons from video sources, many times in which the person is not actively cooperating for the purposes of facial recognition, i.e. "in the wild". [10]
CIFAR-10 is a set of images that can be used to teach a computer how to recognize objects. Since the images in CIFAR-10 are low-resolution (32x32), this dataset can allow researchers to quickly try different algorithms to see what works. CIFAR-10 is a labeled subset of the 80 Million Tiny Images dataset from 2008, published in 2009. When the ...
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.
OPPORTUNITY Activity Recognition Dataset Human Activity Recognition from wearable, object, and ambient sensors is a dataset devised to benchmark human activity recognition algorithms. None. 2551 Text Classification 2012 [188] [189] D. Roggen et al. Real World Activity Recognition Dataset Human Activity Recognition from wearable devices.
Automatic face detection with OpenCV. Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. [1] Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene.