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Randomly sampled color values from face images. B, G, R, values extracted. 245,057 Text Segmentation, classification 2012 [103] [104] R. Bhatt. Bosphorus 3D Face image database. 34 action units and 6 expressions labeled; 24 facial landmarks labeled. 4652 Images, text Face recognition, classification 2008 [105] [106] A Savran et al. UOY 3D-Face
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 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.
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]
Facial recognition works by pinpointing unique dimensions of facial features, which are then rendered as a vector graphic image of the face. Fawkes is a facial image cloaking software created by the SAND (Security, Algorithms, Networking and Data) Laboratory of the University of Chicago . [ 1 ]
Eigenface provides an easy and cheap way to realize face recognition in that: Its training process is completely automatic and easy to code. Eigenface adequately reduces statistical complexity in face image representation. Once eigenfaces of a database are calculated, face recognition can be achieved in real time.
Finding facial landmarks is an important step in facial identification of people in an image. Facial landmarks can also be used to extract information about mood and intention of the person. [ 1 ] Methods used fall in to three categories: holistic methods, constrained local model methods, and regression -based methods.
FACS vectors are used as weights for blend shapes corresponding to each AU, with the resulting face mesh then being used to render the finished face. [25] [26] Deep learning techniques can be used to determine the FACS vectors from face images obtained during motion capture acting, facial motion capture or other performances. [27]
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