In this repository I am sharing demos to test modelling and classification algorithms for face recognition. At the moment the typical pipeline is not implemented in all its components. In particular, I will start with a simple experiment:
- The "The ORL Database of Faces" is used to train and test the system. The ORL database is among the simplest face databases, made up of pictures of 40 individuals, 10 pictures each, for a total of 400 pictures, 92x112 black and white bitmaps. The faces are already aligned and normalized and ready to be processed by a feature extraction algorithm.
- For the training, I am using 5 images, and the remaining 5 images are used for testing through the classificating algorithm. So, in total, 200 training faces for 40 individuals and 200 testing faces.
In the current demo, the model extraction is performed using Dense Convolutional Network in the Pytorch library. One feature vector is extracted for every face using Christian Safka's wrapper library img2vec, the feature is stored in a dictionary in the form {'person1_face1':'vector', 'person1_face2':'vector', ...,'person2:face1':'vector',...}
The classification is performed using different approaches. See the demo folder to learn about the different methods.
- Brute force. Classification is performed iteratively, which is a quite slow approach, but it is sufficient for the sake of showing how a full system can be coded in a few lines of code. The cosin similarity the is most effective distance (
98%of the 200 test faces are recognized) to calculate the similarity of a test vector extracted from the face under test to the face from the training set. - Hierarchical Navigable Small World (HNSW) similarity search is also tested, with the same good results (
98%of the 200 test faces are recognized). - RediSearch Vector Similarity Search. Using this feature, it is possible to index heterogeneous sources of data once converted in their corresponding vector embedding, and then use such index to perform similarity search. Read more in my blog.
┌────────────────┐ ┌────────────────┐ ┌────────────────┐ ┌────────────────┐
│ FACE ├─────►│ NORMALIZATION ├────►│ FEATURE ├───►│ FEATURE │
│ DETECTION │ │ │ │ EXTRACTION │ │ STORE │
└────────────────┘ └────────────────┘ └───────┬────────┘ └───────▲────────┘
│ │
│ ┌───────┴────────┐
└────────────►│ CLASSIFICATION │
└────────────────┘
Clone the repository, setup a virtual environment and run the script:
python3 -m venv myvenv
source myvenv/bin/activate
pip install img2vec_pytorch
pip install scipy
pip install hnswlib
pip install redis
Then run the script iter_test.py to store the vector embeddings in a dictionary and perform iterative classification:
cd demo
python3 iter_test.py
Or run the script hnswlib_test.py to store the embeddings and perform the classification using Hierarchical Navigable Small World:
cd demo
python3 hnswlib_test.py
Finally, you can test the similarity of the test images with the faces modeled in the Redis database using the Vector Similarity Search features. Run the test as follows:
cd demo
python3 vectorsimilarity_test.py
In the future I will test different models, such as multi-vector feature extraction for a single face, or different kind of indexing or even a neural network to speed up the classification of testing images.