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The system uses a deep convolutional neural network to learn a mapping (also called an embedding) from a set of face images to a 128-dimensional Euclidean space, and assesses the similarity between faces based on the square of the Euclidean distance between the images' corresponding normalized vectors in the 128-dimensional Euclidean space.
Puzzle is designed to offer reverse image search visually similar images, even after the images have been resized, re-compressed, recolored and/or slightly modified. [27] The image-match open-source project was released in 2016. The project, licensed under the Apache License, implements a reverse image search engine written in Python. [28]
The pyramid match kernel builds multi-resolution histograms by binning data points into discrete regions of increasing size. Thus, points that do not match at high resolutions have the chance to match at low resolutions. The pyramid match kernel performs an approximate similarity match, without explicit search or computation of distance.
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]
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]
Accordingly, the scale space is analyzed by up-scaling the filter size rather than iteratively reducing the image size. The output of the above 9×9 filter is considered as the initial scale layer at scale s =1.2 (corresponding to Gaussian derivatives with σ = 1.2).
General scheme of content-based image retrieval. Content-based image retrieval, also known as query by image content and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey [1] for a scientific overview of the CBIR field).
The input is an RGB image of the face, scaled to resolution , and the output is a real vector of dimension 4096, being the feature vector of the face image. In the 2014 paper, [ 13 ] an additional fully connected layer is added at the end to classify the face image into one of 4030 possible persons that the network had seen during training time.