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@ -51,6 +51,8 @@ Herein, image path argument could be exact image path, numpy array or base64 enc
**Large Scale Face Recognition** - [`Demo with Elasticsearch`](https://youtu.be/i4GvuOmzKzo), [`Demo with Spotify Annoy`](https://youtu.be/Jpxm914o2xk)
You can store facial embeddings in nosql databases. Here, you can find some implementation experiments with [mongoDb](https://sefiks.com/2021/01/22/deep-face-recognition-with-mongodb/) and [Cassandra](https://sefiks.com/2021/01/24/deep-face-recognition-with-cassandra/). In this way, you can have the power of the map reduce of nosql databases.
Notice that face recognition has O(n) time complexity and this would be problematic for millions level data. Herein, approximate nearest neighbor (a-nn) algorithm reduces the time complexity dramatically. [Spotify Annoy](https://sefiks.com/2020/09/16/large-scale-face-recognition-with-spotify-annoy/), [Facebook Faiss](https://sefiks.com/2020/09/17/large-scale-face-recognition-with-facebook-faiss/) and [NMSLIB](https://sefiks.com/2020/09/19/large-scale-face-recognition-with-nmslib/) are amazing a-nn libraries. Besides, [Elasticsearch](https://sefiks.com/2020/11/27/large-scale-face-recognition-with-elasticsearch/) wraps an a-nn algorithm and it offers highly scalability feature. You should run deepface within those a-nn frameworks if you have really large scale data sets.
**Face recognition models** - [`Demo`](https://youtu.be/i_MOwvhbLdI)