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I'm excited to share a project I've been working on: [GoFaceRec](https://github.com/modanesh/GoFaceRec). This is a face recognition tool built in Go, leveraging the power of MTCNN for face detection and QMagFace for face recognition. The project was born out of a desire to bring the power of deep learning models to the Go community. After much effort, I concluded that the best approach was to convert models to TensorFlow and then work with tfgo, a Go binding to TensorFlow's C API. In GoFaceRec, the input image is first processed, and then its embeddings are compared against the ones already computed from our dataset. If the distance between embeddings falls below a specific threshold, then the face is considered as unknown. Otherwise, the proper label will be printed. The project is tested using Go 1.17 on Ubuntu 20.04. For gocv, the version of OpenCV installed is 4.7. And for tfgo, I installed [this version](https://github.com/galeone/tfgo) instead of the official one. You can install this package by running the following command in your project: > go get github.com/modanesh/GoFaceRec@v0.1.1 You can find more detailed instructions on how to use the tool in the [GitHub repository](https://github.com/modanesh/GoFaceRec). I welcome any feedback, suggestions, or contributions to the project. I'm looking forward to seeing how the community uses GoFaceRec and hope it can be a valuable tool for those working on face recognition tasks. Happy coding! 🚀
I'm excited to share a project I've been working on: [GoFaceRec](https://github.com/modanesh/GoFaceRec). This is a face recognition tool built in Go, leveraging the power of MTCNN for face detection and QMagFace for face recognition. The project was born out of a desire to bring the power of deep learning models to the Go community. After much effort, I concluded that the best approach was to convert models to TensorFlow and then work with tfgo, a Go binding to TensorFlow's C API. In GoFaceRec, the input image is first processed, and then its embeddings are compared against the ones already computed from our dataset. If the distance between embeddings falls below a specific threshold, then the face is considered as unknown. Otherwise, the proper label will be printed. The project is tested using Go 1.17 on Ubuntu 20.04. For gocv, the version of OpenCV installed is 4.7. And for tfgo, I installed [this version](https://github.com/galeone/tfgo) instead of the official one. You can install this package by running the following command in your project: > go get github.com/modanesh/[email protected] You can find more detailed instructions on how to use the tool in the [GitHub repository](https://github.com/modanesh/GoFaceRec). I welcome any feedback, suggestions, or contributions to the project. I'm looking forward to seeing how the community uses GoFaceRec and hope it can be a valuable tool for those working on face recognition tasks. Happy coding! 🚀