Face Detection Solution | Explaining the Core Concept Behind Facial Data Analysis
Author: admin | 19 Jan 2024In This Post
Face recognition has become a necessity across various industries and holds significant value in the biometric verification market. AI is the powerhouse behind this revolutionary technology. It enables businesses to push their limitations when it comes to identity verification and client onboarding. The facial recognition market backs the statement as the number is rising each year. It is expected to reach $13 billion by the end of 2027. In this blog, we will discuss facial detection, which is a primary part of the facial recognition process.
Key Takeaways
- Exploring the impact of face recognition technology across the biometric verification landscape.
- Understanding the difference between face detection and recognition across industries.
- Discussing how modern AI face detection systems work.
- Exploring the impact of pre-trained AI and deep learning models on facial recognition system performance.
How is Face Detection Different From Recognition?
Smartphone cameras have automatic face detection features that are based on face detection technology, which is different from facial recognition. In the past, humans were responsible for detecting human facial subjects, while inventors were working on face recognition solutions. However, advancements in machine intelligence and industry use cases have led to the automation of face detection. This has helped businesses distinguish people from other subjects when using their cameras. AI has played a crucial role in this transformation by ensuring faster and more accurate face detection, even from a far distance away.
Modern AI Face Detection System
In the current business landscape, face recognition has diverse activities to perform other than just verifying people. It is used in fraud and crime prevention, etc. The modern online face detection system is based on artificial intelligence solutions and has three key elements involved.
Neural Network Architecture
Neural networks are key for image processing, specifically for the classification and recognition of objects in digital data. It is the backbone of all AI-powered face detection systems. Convolutional Neural Networks (CNNs) have proven themselves quite reasonable software for businesses to integrate into their organisation for quick face liveness detection. Hardware dependency is highly involved in the operation of neural networks working and it is a significant parameter. Graphical processing units, in line with high-frequency CPUs, help software development companies build better face detection solutions.
Application Programming Interface
Application Programming Interface (API) is the key part of building a face detection system. It connects the front end to the core database where all existing face scan results are stored. Whenever the system client-side receives data from the sensor, the camera, in our case, uses the API to send the request to the server, which in return sends back a response as a “true” or “false.”
Inference Time Range
The third crucial aspect of modern face detection development is the inference time range which helps determine optimal values. This process evaluates the time range when applying the neural network models across the applications in real time.
Working with Pre-Trained Models
In the case of face detection, deep-learning models add more possibilities for accurate detection and classification across human faces. These models are easy to get started with as not every business has the resources to train their neural networks. Most firms opt for pre-trained models to explore new opportunities in the current business landscape. Looking for some ready-to-use models, firms can find many solutions across the market. Each model works on four core principles.
Object Detection
First, the models are well built on detecting and classifying static images of different objects. They can easily distinguish between a human face and other objects. The algorithms set the format or parameters in which they process the image to find the possible matches of human faces. After finding the face-like objects within the image, the software cuts it to the size of the image and sends the data to the server for feature extraction.
Data Alignment
Before proceeding to feature extraction of the object, the model also aligns the data as per the back-end format for seamless processing. It converts the digital outputs into machine-readable format. Images from different perspectives, angles, and lighting conditions are sent to processing after aligning the data to reduce the influence of any internal performance lag. This is a crucial step in AI face detection models.
Feature Extraction
In this step of the face detection model, the machine learning model works on the object to extract its features. It takes account of all the embeddings mentioned above and identifies each element separately until it can mark the object as a human face. This process is fast as the neural networks are built on highly efficient algorithms, ensuring excellent performance. Mostly, a single object set of facial embeddings contains 128 unique face features that the system extracts from the image to distinguish it successfully.
Feature Identification
In the last step of face detection, the system compares the extracted features with the data already present in the database. It performs mathematical calculations to ensure that the image contains a face. All the modern face detection systems present in hi-tech cams, smartphones and other facial recognition devices use these 4 steps to accurately distinguish faces from other real-world objects.
Where Facia Fits Into the Puzzle
Face detection and recognition have come a long way compared to the early days of biometric verification. The adoption of the technologies across digital devices such as mobile phones states the importance of this approach. At Facia, we believe in the power of automation and AI to bring the best possible face detection and recognition solutions to the business industry.
With a large number of use cases, our world-class AI models are trained on highly specialised deep learning algorithms that make up the best face identification systems in the market. Looking to incorporate a face recognition systems within your organisation, or looking to enhance your application’s offerings with our highly intelligent AI model?
Contact us now! We also offer a free demo of our face recognition model. Talk to our experts now to get a complete walkthrough for your specific use case.
Frequently Asked Questions
Face detection is a technology that identifies human faces within digital images, distinguishing them from other objects. This is crucial for developing anti-spoofing solutions, which help protect businesses from fraud by ensuring that facial recognition systems interact with real human faces rather than photographs or masks.
Face detection leverages artificial intelligence to recognize human faces in images or video streams. It processes facial data by identifying distinguishing features like the eyes, nose, mouth, and jawline. Algorithms analyze these features to detect and confirm the presence of a face.
The future of face detection is promising as it continues to integrate into various business sectors. With advancements in AI and machine learning, face detection is expected to become faster, more accurate, and capable of handling complex facial recognition tasks in real-time (30fps), enhancing security and user experience.
Accuracy in face detection heavily relies on the underlying model and the quality of its training data. Models trained on vast, diverse datasets, like Morpheus by Facia, perform exceptionally well. It can accurately detect faces (around 95%) across different lighting conditions, angles, and facial expressions, exceeding 99.5% in controlled environments.
AI, especially through deep learning techniques, is crucial in facial recognition. It processes large datasets of diverse facial images, swiftly and accurately learning to identify and verify individual faces. AI boosts the system's efficiency and speed, significantly reducing delays and enhancing the overall effectiveness of facial recognition applications.
