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Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]
Numpy is one of the most popular Python data libraries, and TensorFlow offers integration and compatibility with its data structures. [66] Numpy NDarrays, the library's native datatype, are automatically converted to TensorFlow Tensors in TF operations; the same is also true vice versa. [66]
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.
Facial recognition systems have been deployed in advanced human–computer interaction, video surveillance, law enforcement, passenger screening, decisions on employment and housing and automatic indexing of images. [4] [5] Facial recognition systems are employed throughout the world today by governments and private companies. [6]
Face detection, often a step done before facial recognition; Face perception, the process by which the human brain understands and interprets the face; Pareidolia, which involves, in part, seeing images of faces in clouds and other scenes; Facial recognition system, an automated system with the ability to identify individuals by their facial ...
Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. [1] These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development. [1]
Three-dimensional face recognition (3D face recognition) is a modality of facial recognition methods in which the three-dimensional geometry of the human face is used. It has been shown that 3D face recognition methods can achieve significantly higher accuracy than their 2D counterparts, rivaling fingerprint recognition .
Nevertheless, a feature is typically defined as an "interesting" part of an image, and features are used as a starting point for many computer vision algorithms. Since features are used as the starting point and main primitives for subsequent algorithms, the overall algorithm will often only be as good as its feature detector.