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https://github.com/soumik12345/nearest-celebrity-face
Tensorflow Implementation of FaceNet: A Unified Embedding for Face Recognition and Clustering to find the celebrity whose face matches the closest to yours.
https://github.com/soumik12345/nearest-celebrity-face
deep-learning deep-neural-networks deeplearning face face-recognition facenet facenet-model inception inception-resnet-v2 inceptionv2 keras keras-neural-networks keras-tensorflow machine-learning machinelearning meta-learning one-shot-learning python python3 tensorflow
Last synced: 8 days ago
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Tensorflow Implementation of FaceNet: A Unified Embedding for Face Recognition and Clustering to find the celebrity whose face matches the closest to yours.
- Host: GitHub
- URL: https://github.com/soumik12345/nearest-celebrity-face
- Owner: soumik12345
- Created: 2018-12-05T06:15:29.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T02:59:57.000Z (about 2 years ago)
- Last Synced: 2025-01-07T01:32:29.906Z (14 days ago)
- Topics: deep-learning, deep-neural-networks, deeplearning, face, face-recognition, facenet, facenet-model, inception, inception-resnet-v2, inceptionv2, keras, keras-neural-networks, keras-tensorflow, machine-learning, machinelearning, meta-learning, one-shot-learning, python, python3, tensorflow
- Language: Jupyter Notebook
- Homepage: https://soumik12345.github.io/geekyrakshit-blog/computervision/deeplearning/facenet/inception/keras/nearestcelebrityface/python/tensorflow/2019/08/07/nearest-celebrity-face.html
- Size: 25.6 MB
- Stars: 31
- Watchers: 3
- Forks: 7
- Open Issues: 24
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Nearest-Celebrity-Face
[](http://hits.dwyl.com/soumik12345/Nearest-Celebrity-Face)
[](https://paperswithcode.com/sota/face-verification-on-ijb-c?p=facenet-a-unified-embedding-for-face)
[](https://paperswithcode.com/sota/face-verification-on-labeled-faces-in-the?p=facenet-a-unified-embedding-for-face)
[](https://paperswithcode.com/sota/face-verification-on-megaface?p=facenet-a-unified-embedding-for-face)
[](https://paperswithcode.com/sota/face-identification-on-megaface?p=facenet-a-unified-embedding-for-face)
[](https://paperswithcode.com/sota/face-verification-on-youtube-faces-db?p=facenet-a-unified-embedding-for-face)
### Overview
Implementation of [FaceNet: A Unified Embedding for Face Recognition and Clustering
](https://arxiv.org/abs/1503.03832v3) to find the celebrity whose face matches the closest to yours.
The input face is encoded with a pretrained inception model into a vector and then its geometric distance is calculated with the encoded vectors of all the images present in the dataset and the image with the least distance is selected.
**Article Link:** [https://soumik12345.github.io/geekyrakshit-blog/computervision/deeplearning/facenet/inception/keras/nearestcelebrityface/python/tensorflow/2019/08/07/nearest-celebrity-face.html](https://soumik12345.github.io/geekyrakshit-blog/computervision/deeplearning/facenet/inception/keras/nearestcelebrityface/python/tensorflow/2019/08/07/nearest-celebrity-face.html)
### Installation
1. Create a new conda environment using `conda create --name nearest_celeb_face`
2. Activate the `activate nearest_celeb_face` if you are on Windows and `source activate nearest_celeb_face` if you are on Linux
3. Clone the repository using `git clone https://github.com/soumik12345/Nearest-Celebrity-Face`
4. Enter the root directory using `cd Nearest-Celebrity-Face`
5. Install the required dependencies using `pip install -r requirements.txt`
### Usage
The `TestCases` folder contains two folders `Actual` that contains full sized images of individuals and `Preprocessed` containing the faces manually cropped out of the full sized images. Any number of testcases can be added provided that one image each is present in the `Actual` and `Preprocessed` folders with the exact same filename and the preprocessed image should have the face manually cropped out for best performance. Refer to the already cropped images for further detail on how to crop. Once the tescases are setup, run `python main.py` or `python3 main.py` to run the program.
### Sample Outputs
### Citation
```
@article{1503.03832,
Author = {Florian Schroff and Dmitry Kalenichenko and James Philbin},
Title = {FaceNet: A Unified Embedding for Face Recognition and Clustering},
Year = {2015},
Eprint = {arXiv:1503.03832},
Doi = {10.1109/CVPR.2015.7298682},
}
```