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The Viola–Jones object detection framework is a machine learning object detection framework proposed in 2001 by Paul Viola and Michael Jones. [1] [2] It was motivated primarily by the problem of face detection, although it can be adapted to the detection of other object classes.
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]
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]
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.
Deeplearning4j can be used via multiple API languages including Java, Scala, Python, Clojure and Kotlin. Its Scala API is called ScalNet. [31] Keras serves as its Python API. [32] And its Clojure wrapper is known as DL4CLJ. [33] The core languages performing the large-scale mathematical operations necessary for deep learning are C, C++ and CUDA C.
Face detection is gaining the interest of marketers. A webcam can be integrated into a television and detect any face that walks by. The system then calculates the race, gender, and age range of the face. Once the information is collected, a series of advertisements can be played that is specific toward the detected race/gender/age.
OpenVINO IR [5] is the default format used to run inference. It is saved as a set of two files, *.bin and *.xml, containing weights and topology, respectively.It is obtained by converting a model from one of the supported frameworks, using the application's API or a dedicated converter.
The technique used in creating eigenfaces and using them for recognition is also used outside of face recognition: handwriting recognition, lip reading, voice recognition, sign language/hand gestures interpretation and medical imaging analysis. Therefore, some do not use the term eigenface, but prefer to use 'eigenimage'.