facenet
deepface
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facenet | deepface | |
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5 | 14 | |
13,493 | 9,920 | |
- | - | |
0.0 | 9.5 | |
9 months ago | 9 days ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
facenet
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CompreFace - Free and open-source self-hosted face recognition system from Exadel
As for me, openface is already outdated - the latest release was in 2016. If you look for a library, the easiest to use is ageitgey/face_recognition. The more accurate libraries are davidsandberg/facenet and deepinsight/insightface.
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Facial recognition using cluster
ML training is practically impossible on micro-controllers. Inferencing on the other hand is quite doable, especially if aided by a [TPU coprocessor](https://coral.ai/products/accelerator/). Supposedly with the TPU you can do some quantization-aware training, but I haven't tried this. I am working on a security system that does facial recognition to recognize me and some friends and considers anyone else as an intruder. How I am doing this is by retraining [Facenet](https://github.com/davidsandberg/facenet) with my facial embeddings. Use something like Haar Cascade in OpenCV to get the bounding box for a face and put it through the model to extract face embeddings. You can then save these embeddings as a sort of databases for the faces you want it to recognize during the inferencing phase. After that you can impose something like a SVM classifier to say who in your face database it is. One thing I will note is that the problem is even easier if you are only concerned with one face - in which case it is technically face identification - not recognition. If that is the case, you only need to do a difference calculation between the embeddings you saved during training and the result output from inferencing. If you do end up using the TPU, you can connect to it over USB from inside a container (I only know how to do this in Docker though) too. Hope this was helpful. I am actually looking to use a k8s cluster eventually too as a sort of smart hub for my security system and other devices so I can handle much more traffic (not sure if this is overkill or not on the pi 4s).
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Man with foot up on desk in Pelosi's office at Capitol arrested
He might just be a solid techie because the scripts are freely available on github. https://github.com/davidsandberg/facenet
deepface
- AI face similarity checker
- [D] Fast face recognition over video
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How to do Human Head Segmentation from images?
You might also want to categorize using either mediapose's holistic model which includes the face mesh or maybe something that gauges emotion like this: https://github.com/serengil/deepface
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ai vs human guess age.
This is another one I tried but requires programming knowledge. https://github.com/serengil/deepface
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A novel approach to SD animation
It must be a matter of time before we have this, we have so many library's that can detect a face expression. For example: https://github.com/serengil/deepface
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DAM with face recognition
I bought a On1 license, but as you point out, it has no facial recognition nor any plans to add it. That also means it has no concept of face regions as implemented in XMP, so that cannot be retrofitted by running face recognition externally, e.g. using an open-source solution like Deepface.
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Any algorithm to get coordinates of classified face?
deepface - newer version which has multiple face detectors in it you can mess with, dlib included. Also does face recognition.
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hey guys which is the best tool for making facial recognition using single image in deep learning
If you're looking for models / planning to write some code yourself: Have a look at ArcFace for recognition and RetinaFace for face detection by Insightface. DeepFace repository might also be worth a try.
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Models for facial identification?
DeepFace: https://github.com/serengil/deepface
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Best Face Landmark Detection models
DeepFace utilizes a collection of different face detection models listed here: models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace"]
What are some alternatives?
insightface - State-of-the-art 2D and 3D Face Analysis Project
Face Recognition - The world's simplest facial recognition api for Python and the command line
facematch - Facematch is a tool to verifies if two photos contain the same person.
CompreFace - Leading free and open-source face recognition system
textgenrnn - Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
DeepStack - The World's Leading Cross Platform AI Engine for Edge Devices
yolov8-face - yolov8 face detection with landmark
anime-face-detector - Anime Face Detector using mmdet and mmpose
EagleEye - Stalk your Friends. Find their Instagram, FB and Twitter Profiles using Image Recognition and Reverse Image Search.
facenet-pytorch - Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models