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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
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 .
PimEyes is a facial recognition search website that allows users to identify all images on the internet of a person given a sample image. The website is owned by EMEARobotics, a corporation based in Dubai. The owner and CEO of EMEARobotics and PimEye is Giorgi Gobronidze, who is based in Tbilisi, Georgia. [1]
The creators of Fawkes identify, that using sybil images can increase the effectiveness of their software against recognition software products. Sybil images are images that do not match the person they are attributed to. This confuses the facial recognition software and leads to misidientification which also helps the efficacy of image cloaking.
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.
[1] [2] It was motivated primarily by the problem of face detection, although it can be adapted to the detection of other object classes. In short, it consists of a sequence of classifiers. Each classifier is a single perceptron with several binary masks (Haar features). To detect faces in an image, a sliding window is computed over the image.
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.