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  2. Object detection - Wikipedia

    en.wikipedia.org/wiki/Object_detection

    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]

  3. DeepFace - Wikipedia

    en.wikipedia.org/wiki/DeepFace

    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.

  4. Face detection - Wikipedia

    en.wikipedia.org/wiki/Face_detection

    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.

  5. Higher-order singular value decomposition - Wikipedia

    en.wikipedia.org/wiki/Higher-order_singular...

    The power of the tensor framework was showcased by decomposing and representing an image in terms of its causal factors of data formation, in the context of Human Motion Signatures for gait recognition, [18] face recognition—TensorFaces [19] [20] and computer graphics—TensorTextures.

  6. FaceNet - Wikipedia

    en.wikipedia.org/wiki/FaceNet

    FaceNet is a facial recognition system developed by Florian Schroff, Dmitry Kalenichenko and James Philbina, a group of researchers affiliated with Google.The system was first presented at the 2015 IEEE Conference on Computer Vision and Pattern Recognition. [1]

  7. Facial recognition system - Wikipedia

    en.wikipedia.org/wiki/Facial_recognition_system

    Some face recognition algorithms identify facial features by extracting landmarks, or features, from an image of the subject's face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. [36] These features are then used to search for other images with matching features. [37]

  8. Gesture recognition - Wikipedia

    en.wikipedia.org/wiki/Gesture_recognition

    Gesture recognition is an area of research and development in computer science and language technology concerned with the recognition and interpretation of human gestures. A subdiscipline of computer vision , [ citation needed ] it employs mathematical algorithms to interpret gestures.

  9. Outline of object recognition - Wikipedia

    en.wikipedia.org/wiki/Outline_of_object_recognition

    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.