FaceNet is a facial recognition system developed by Florian Schroff, Dmitry Kalenichenko and James Philbina, a group of researchers affiliated to Google. The system was first presented in the IEEE Conference on Computer Vision and Pattern Recognition held in 2015.[1] The system uses a deep convolutional neural network to learn a mapping (also called an embedding) from a set of face images to the 128-dimensional Euclidean space and the similarity between two face images is assessed based on the square of the Euclidean distance between the corresponding normalized vectors in the 128-dimensional Euclidean space. The system used the triplet loss function as the cost function and introduced a new online triplet mining method. The system achieved an accuracy of 99.63% which is the highest score on Labeled Faces in the Wild dataset in the unrestricted with labeled outside data protocol.[2]
^Florian Schroff; Dmitry Kalenichenko; James Philbin. "FaceNet: A Unified Embedding for Face Recognition and Clustering" (PDF). The Computer Vision Foundation. Retrieved 4 October 2023.
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score on Labeled Faces in the Wild dataset in the unrestricted with labeled outside data protocol. The structure of the FaceNetfacenet recognition system...
technology, FaceNet is more successful than DeepFace using the same data sets. FaceNet set a record for accuracy, 99.63%. Google's FaceNet incorporates...
performance. After normalizing embeddings to the surface of a hypersphere, FaceNet achieves the best performance using 128 dimensions as opposed to 64, 256...
Schroff, F.; Kalenichenko, D.; Philbin, J. (June 2015). "FaceNet: A unified embedding for face recognition and clustering". 2015 IEEE Conference on Computer...
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unified embedding for face recognition and clustering CNN (“Facenet”), Labeled Faces in the Wild (LFW) (source), and other open source faces, private biometric...
body language Computer vision DeepFaceFaceNetFace perception Face Recognition Grand Challenge FindFace Glasgow Face Matching Test ISO/IEC 19794-5 MALINTENT...
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