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DeepFace is a deep learning facial recognition system created by a research group at Facebook. It identifies human faces in digital images. The program employs a nine-layer neural network with over 120 million connection weights and was trained on four million images uploaded by Facebook users. [1][2] The Facebook Research team has stated that ...
Facial recognition software at a US airport. Automatic ticket gate with face recognition system in Osaka Metro Morinomiya Station. A facial recognition system[ 1 ] is a technology potentially capable of matching a human face from a digital image or a video frame against a database of faces. Such a system is typically employed to authenticate ...
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 emotion annotation can be done in ...
Informally, eigenfaces can be considered a set of "standardized face ingredients", derived from statistical analysis of many pictures of faces. Any human face can be considered to be a combination of these standard faces. For example, one's face might be composed of the average face plus 10% from eigenface 1, 55% from eigenface 2, and even −3 ...
Image matches with the image stores in database. Any facial feature changes in the database will invalidate the matching process. [3] A reliable face-detection approach based on the genetic algorithm and the eigen-face [4] technique: Firstly, the possible human eye regions are detected by testing all the valley regions in the gray-level image.
Haar-like features are digital image features used in object recognition. They owe their name to their intuitive similarity with Haar wavelets and were used in the first real-time face detector. [1] Historically, working with only image intensities (i.e., the RGB pixel values at each and every pixel of image) made the task of feature ...
Digital image processing is the use of a digital computer to process digital images through an algorithm. [1][2] As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such ...
Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes and scales or even when they are translated or rotated.
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