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Onnowpurbo (talk | contribs) Created page with "Sumber: https://github.com/TadasBaltrusaitis/OpenFace/wiki Over the past few years, there has been an increased interest in automatic facial behavior analysis and understand..." |
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Sumber: https://github.com/TadasBaltrusaitis/OpenFace/wiki | Sumber: | ||
* https://github.com/TadasBaltrusaitis/OpenFace/wiki | |||
* https://cmusatyalab.github.io/openface/ | |||
OpenFace | OpenFace adalah implementasi pengenalan wajah menggunakan Python dan Torch dengan deep neural network dan berdasarkan pada CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering oleh Florian Schroff, Dmitry Kalenichenko, dan James Philbin di Google. Torch memungkinkan jaringan dieksekusi pada CPU atau dengan CUDA. | ||
Dibuat oleh Brandon Amos, Bartosz Ludwiczuk, dan Mahadev Satyanarayanan. | |||
* Source tersedia di GitHub pada cmusatyalab/openface. | |||
* Ada dokumentasi API | |||
* Bergabung pada group cmu-openface atau gitter chat untuk diskusi dan masalah instanasi. | |||
Penelitian ini di dukung oleh National Science Foundation (NSF) under grant number CNS-1518865. Additional support was provided by the Intel Corporation, Google, Vodafone, NVIDIA, and the Conklin Kistler family fund. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and should not be attributed to their employers or funding sources. | |||
Isn't face recognition a solved problem? | |||
No! Accuracies from research papers have just begun to surpass human accuracies on some benchmarks. The accuracies of open source face recognition systems lag behind the state-of-the-art. See our accuracy comparisons on the famous LFW benchmark. | |||
Please use responsibly! | |||
We do not support the use of this project in applications that violate privacy and security. We are using this to help cognitively impaired users sense and understand the world around them. | |||
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. | |||
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* https://github.com/TadasBaltrusaitis/OpenFace/wiki | * https://github.com/TadasBaltrusaitis/OpenFace/wiki | ||
* https://cmusatyalab.github.io/openface/ | |||
Revision as of 10:24, 24 May 2018
Sumber:
OpenFace adalah implementasi pengenalan wajah menggunakan Python dan Torch dengan deep neural network dan berdasarkan pada CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering oleh Florian Schroff, Dmitry Kalenichenko, dan James Philbin di Google. Torch memungkinkan jaringan dieksekusi pada CPU atau dengan CUDA.
Dibuat oleh Brandon Amos, Bartosz Ludwiczuk, dan Mahadev Satyanarayanan.
- Source tersedia di GitHub pada cmusatyalab/openface.
- Ada dokumentasi API
- Bergabung pada group cmu-openface atau gitter chat untuk diskusi dan masalah instanasi.
Penelitian ini di dukung oleh National Science Foundation (NSF) under grant number CNS-1518865. Additional support was provided by the Intel Corporation, Google, Vodafone, NVIDIA, and the Conklin Kistler family fund. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and should not be attributed to their employers or funding sources.
Isn't face recognition a solved problem?
No! Accuracies from research papers have just begun to surpass human accuracies on some benchmarks. The accuracies of open source face recognition systems lag behind the state-of-the-art. See our accuracy comparisons on the famous LFW benchmark. Please use responsibly!
We do not support the use of this project in applications that violate privacy and security. We are using this to help cognitively impaired users sense and understand the world around them. 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.
