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PUBLIC MARKS from decembre with tag "face recognition"

27 November 2017 10:00

FACE RECOGNITION - OpenFace project

Overview: The following overview shows the workflow for a single input image of Sylvestor Stallone from the publicly available LFW dataset. Detect faces with a pre-trained models from dlib or OpenCV. Transform the face for the neural network. This repository uses dlib's real-time pose estimation with OpenCV's affine transformation to try to make the eyes and bottom lip appear in the same location on each image. Use a deep neural network to represent (or embed) the face on a 128-dimensional unit hypersphere. The embedding is a generic representation for anybody's face. Unlike other face representations, this embedding has the nice property that a larger distance between two face embeddings means that the faces are likely not of the same person. This property makes clustering, similarity detection, and classification tasks easier than other face recognition techniques where the Euclidean distance between features is not meaningful. Apply your favorite clustering or classification techniques to the features to complete your recognition task. See below for our examples for classification and similarity detection, including an online web demo.

27 November 2017 04:00

FACE RECOGNITION - DATA SET - The FaceScrub datase - vintage - resources

A Dataset With Over 100,000 Face Images of 530 People. Large face datasets are important for advancing face recognition research, but they are tedious to build, because a lot of work has to go into cleaning the huge amount of raw data. To facilitate this task, we developed an approach to building face datasets that detects faces in images returned from searches for public figures on the Internet, followed by automatically discarding those not belonging to each queried person. The FaceScrub dataset was created using this approach, followed by manually checking and cleaning the results. It comprises a total of 106,863 face images* of male and female 530 celebrities, with about 200 images per person. As such, it is one of the largest public face databases. The images were retrieved from the Internet and are taken under real-world situations (uncontrolled conditions). Name and gender annotations of the faces are included.

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