Building a facial recognition system using Elasticsearch and Python involves several key steps, including face detection, encoding facial features into a 128-dimension vector, and storing these vectors using Elasticsearch's dense_vector data type. The process starts with using Python libraries such as face_recognition and numpy to detect faces in an image and encode them into vectors. These vectors are then stored in an Elasticsearch index, which allows for efficient searching and comparison using functions like cosineSimilarity to find matches between different facial representations. The tutorial demonstrates how to set up the necessary environment with Python and Elasticsearch, create and index facial data, and perform searches to find matching faces. It also highlights the potential for integrating this system with advanced search queries in Elasticsearch, enabling complex use cases beyond basic facial recognition, such as combining geo-queries with cosine similarity for enhanced search capabilities.