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@ -51,9 +51,9 @@ 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. In this way, you can have the power of the map reduce technology. Here, you can find some implementation experiments with [mongoDb](https://sefiks.com/2021/01/22/deep-face-recognition-with-mongodb/), [Cassandra](https://sefiks.com/2021/01/24/deep-face-recognition-with-cassandra/) and [Hadoop](https://sefiks.com/2021/01/31/deep-face-recognition-with-hadoop-and-spark/).
Notice that face recognition has O(n) time complexity and this becomes problematic for millions level data and limited hardware. If you have a really strong database, then you use [relational databases and regular SQL](https://sefiks.com/2021/02/06/deep-face-recognition-with-sql/). Besides, you can store facial embeddings in nosql databases. That's a better way. In this way, you can have the power of the map reduce technology. Here, you can find some implementation experiments with [mongoDb](https://sefiks.com/2021/01/22/deep-face-recognition-with-mongodb/), [Cassandra](https://sefiks.com/2021/01/24/deep-face-recognition-with-cassandra/) and [Hadoop](https://sefiks.com/2021/01/31/deep-face-recognition-with-hadoop-and-spark/).
Notice that face recognition has O(n) time complexity and this would be problematic for millions level data and limited hardware. 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.
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)