Building an Application for Facial Recognition Using Python, OpenCV, Transformers and Qdrant
Tags: qdrant, opencv, transformers
Published: 2023-12-29 05:17:29


![Face Regonition Application Workflow](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/n8jc4yf04nr9yjbclaqt.png)
**Method 1. Facial Recognition Using Python, OpenCV and Qdrant.**
Facial recognition technology has become a ubiquitous force, reshaping industries like security, social media, and smartphone authentication. In this blog, we dive into the captivating realm of facial recognition armed with the formidable trio of Python, OpenCV, image embeddings and Qdrant. Join us on this journey as we unravel the intricacies of creating a robust facial recognition system.

**Part 1: An Introduction to Facial Recognition**
In Part 1, we lay the foundation by delving into the fundamentals of facial recognition technology. Understand the underlying principles, explore its applications, and grasp the significance of Python and OpenCV in our development stack.

**Part 2: Setting Up the Environment**
A crucial step in any project is preparing the development environment. Learn how to seamlessly integrate Python, OpenCV, and Qdrant to create a harmonious ecosystem for our facial recognition system. We provide step-by-step instructions, ensuring you have solid groundwork before moving forward.

**Part 3: Implementation of Facial Recognition Algorithms**
With the groundwork in place, we dive into the core of the project. Explore the intricacies of facial recognition algorithms and witness the magic unfold as we implement them using Python and OpenCV. Uncover the inner workings of face detection, feature extraction, and model training.

**Part 4: Database Integration with Qdrant**
No facial recognition system is complete without a robust database to store and manage facial data efficiently. In the final installment, we guide you through the integration of Qdrant, to enhance the storage and retrieval capabilities of our system. Witness the synergy between Python, OpenCV, and Qdrant as we bring our project to its culmination.

By the end of this blog, you would have gained a comprehensive understanding of facial recognition technology and the practical skills to develop your own system.

**Step-by-Step Implementation**

- Download all the pictures of interest into a local folder.
- Identify and extract faces from the pictures.
- Calculate facial embeddings from the extracted faces.
- Store these facial embeddings in a Qdrant database.
- Obtain a colleague’s picture for identification purposes.
- Match the face with the provided picture.
- Calculate embeddings for the identified face in the provided picture.
- Utilize the Qdrant distance function to retrieve the closest matching faces and corresponding photos from the database.

This experiment demonstrates the practical implementation of Python OpenCV and advanced AI technologies in creating a sophisticated Facial recognition / Search Application, showcasing the potential for enhanced user interactions and cognitive responses. Since images are sensitive data ,we do not want to rely on any online service or upload them onto the internet. The entire pipeline defined above is developed to work 100% locally.

**The Technology Stack**

- **Qdrant:** _Vector store for storing image embeddings._
- **OpenCV:** _Detect faces from the images. To “extract” faces from the pictures we used Python, OpenCV, a computer vision tool, and a pre-trained Haar Cascade model._
- **imgbeddings:** _A Python package to generate embedding vectors from images, using OpenAI’s robust CLIP model via Hugging Face transformers._

**An Overview of OpenCV**

OpenCV, or Open Source Computer Vision Library, is an open-source computer vision and machine learning software library. Originally developed by Intel, OpenCV is now maintained by a community of developers. It provides a wide range of tools and functions for image and video analysis, including various algorithms for image processing, computer vision, and machine learning.

**Key features of OpenCV include:**

- **Image Processing:** _OpenCV offers a plethora of functions for basic and advanced image processing tasks, such as filtering, transformation, and color manipulation._
- **Computer Vision Algorithms:** _The library includes implementation of various computer vision algorithms, including feature detection, object recognition, and image stitching.
- **Machine Learning:** OpenCV integrates with machine learning frameworks and provides tools for training and deploying machine learning models. This is particularly useful for tasks like object detection and facial recognition._
- **Camera Calibration:** _OpenCV includes functions for camera calibration, essential in computer vision applications to correct distortions caused by camera lenses._
- **Real-time Computer Vision:** _It supports real-time computer vision applications, making it suitable for tasks like video analysis, motion tracking, and augmented reality._
- **Cross-Platform Support:** _OpenCV is compatible with various operating systems, including Windows, Linux, macOS, Android, and iOS. This makes it versatile for a wide range of applications._
- **Community Support:** _With a large and active community, OpenCV is continuously evolving, with contributions from researchers, developers, and engineers worldwide._

OpenCV is widely used in academia, industry, and research for tasks ranging from simple image manipulation to complex computer vision and machine learning applications. Its versatility and comprehensive set of tools make it a go-to library for developers working in the field of computer vision.

**An Overview of imgbeddings**
Here’s a Python package to generate embedding vectors from images, using OpenAI’s robust CLIP model via Hugging Face transformers. These image embeddings, derived from an image model that has seen the entire internet up to mid-2020, can be used for many things: unsupervised clustering (e.g. via umap), embeddings search (e.g. via faiss), and using downstream for other framework-agnostic ML/AI tasks such as building a classifier or calculating image similarity.

The embeddings generation models are ONNX INT8-quantized — meaning they’re 20–30% faster on the CPU, much smaller on disk, and don’t require PyTorch or TensorFlow as a dependency!
Works for many different image domains, thanks to CLIP’s zero-shot performance.
Includes utilities for using principal component analysis (PCA) to reduce the dimensionality of generated embeddings without losing much info.

**Vector Store Explained**

**Definition**
Vector stores are specialized databases designed for efficient storage and retrieval of vector embeddings. This specialization is crucial, as conventional databases like SQL are not finely tuned for handling extensive vector data.

**Role of Embeddings**
Embeddings represent data, typically unstructured data like text or images, in numerical vector formats within a high-dimensional space. Traditional relational databases are ill-suited for storing and retrieving these vector representations.

**Key Features of Vector Stores**

- **Efficient Indexing:** Vector stores can index and rapidly search for similar vectors using similarity algorithms.
- **Enhanced Retrieval:** This functionality allows applications to identify related vectors based on a provided target vector query.

**An Overview of Qdrant**

Getting started with Qdrant is seamless. Utilize the Python qdrant-client, access the latest Docker image of Qdrant and establish a local connection, or explore Qdrant’s Cloud free tier option until you are prepared for a comprehensive transition.

**High-Level Qdrant Architecture**


![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/mhwt8diy2fdrnyp56xwh.png)

**Understanding Semantic Similarity**
Semantic similarity, in the context of a set of documents or terms, is a metric that gauges the distance between items based on the similarity of their meaning or semantic content, rather than relying on lexicographical similarities. This involves employing mathematical tools to assess the strength of the semantic relationship between language units, concepts, or instances. The numerical description obtained through this process results from comparing the information that supports their meaning or describes their nature.

It’s crucial to distinguish between semantic similarity and semantic relatedness. Semantic relatedness encompasses any relation between two terms, whereas semantic similarity specifically involves ‘is a’ relation. This distinction clarifies the nuanced nature of semantic comparisons and their application in various linguistic and conceptual contexts.


**Method 2. Using Transformers and Qdrant for Image Recognition**
Apart from OpenCV, we can also use Vision Transformers to perform the same task. 

For detailed code implementation please refer [here](https://medium.com/@nayakpplaban/building-an-application-for-facial-recognition-using-python-opencv-transformers-and-qdrant-a144871f40d9)
References
https://qdrant.tech/documentation
https://github.com/opencv/opencv
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