Face Detection Algorithm: How Does It Work for Facial Biometrics?

You just whip out your smartphone, aim the camera, and capture that happy-go-lucky face of yours, complete with shiny eyes or perfectly applied makeup. And voilà! In just a second, you’re detected!

Everyone loves this feature because there’s no need to awkwardly poke and prod at the screen—Of course, I’m a fan too!

Want to try out some virtual cosmetics for that nude look? Use face detection-powered lifelike filters.

Need to tone face muscles with some face yoga for sharp jawlines or to tackle pigmentation? There are apps that give spot-on recommendations using facial detection.

Look around, and you’ll find a ton of mind-blowing applications out there! But have you ever wondered what magic happens behind the scenes with face detection?

If not, no worries! Today, you’re in for a treat with a detailed overview of face detection algorithms that won’t be too technical for you. Get ready to dive in!

Best Algorithms that Work Behind Face Detection Technology

There are various algorithm components that make face detection more smoother and simply reliable. However, this list is curated the most popular and convenient one:

Eigenfaces

Let's first start with the Eigenfaces face detection algorithm that acts on face variances in image data sets to encode and decode those faces. While it uses mathematical values to process statistical databases, it doesn't use any digital images. And, each facial image in the dataset is treated as a high-dimensional vector, where each pixel represents a feature.

This works best for the small dataset and under controlled conditions where no sensitive changes are present, likely in position, occlusion, lighting, etc.

Fisherfaces

The Fisherfaces facial detection algorithm is the upgraded version of Eigenfaces, which uses Linear Discriminant Analysis (LDA) instead of Principal Component Analysis (PCA). It was developed to address some of the limitations of Eigenfaces, particularly sensitivity to lighting and facial expression variations.

Fisherfaces provides better class separability with 93% accuracy, making it more effective at distinguishing between faces of different individuals under varying conditions.

Adaptive Neuro-Fuzzy Inference System (ANFIS) 

The ANFIS algorithm for face detection is the combination of a hybrid approach, including Artificial Neural Networks (ANN) with fuzzy logic principles in a single structure. ANFIS is particularly useful in tasks that require flexible and adaptive systems. And, if we look at its accuracy rate, it falls somewhere around 97.1% after feature extraction with 2D principal component analysis.

Viola-Jones

The Viola-Jones face detection algorithm is one of the most old-school but widely used and classic methods to detect faces from images and videos. This algorithm is painfully slow to train but can detect faces in real time with impressive speed.

This algorithm follows four main steps to work - 

1. Haar feature selection to represent patterns commonly found in faces, 

2. Integral image representation to calculate the sum of all pixels in a rectangle face region, 

3. AdaBoost to distinguish between faces and non-faces, and 

4. Cascade structure to reject most non-face regions very quickly.

Haar Cascade

The Haar Cascade face detection algorithm is a machine learning-based approach used for real-time object detection, most commonly applied to face detection. This algorithm is usually trained to understand a large number of positive (faces) and negative samples (non-faces). It uses edge or line detection features to find edges or lines in the image, identify sudden changes in pixel intensities, and detect facial structures.

While it can boost the speed of face detection, it struggles with rotated faces or faces that are not perfectly aligned (non-frontal).

Convolutional Neural Network (CNN)

CNN algorithm just revolutionized old-age facial detection with its ability to automatically learn complex features from data while using multiple layers. CNN can have dozens and hundreds of these layers, and each of them learns to detect different imaging features.

However, here's a quick go-through to understand some of them-

• It uses convolutional layer to learn visual patterns in an image, such as edges, shapes, and textures,
• Next, it uses a pooling layer to reduce the spatial size of the feature maps, 
• Its fully connected layers help consolidate the detected features and make a final classification, and
• Finally, the bounding box regression refines these initial predictions, accurately frames the detected face, and provides a confidence score, indicating the likelihood of a face being present in the detected region.

Single Shot Detector (SSD)

While object detection is required and it becomes a critical task, the SSD face detection algorithm enables it, making it well-suited for real-time applications like video surveillance and mobile-based face detection.

To process this task, it follows some steps, likely, input image resizing and feature extraction, multi-scale facial feature mapping, bounding box regression, and confidence scoring. This approach not only speeds up the detection process but also maintains high accuracy, making SSD a popular choice for real-time applications.

3 Different Example to Understand Real-time Use Cases of Face Detection 

Today, almost all applications are built-in with face detection techniques and keeping that in mind, we’ve highlighted various types of practical utilization of face detection.

Lystface — Location based facial attendance 

Lystface is a facial detection based attendance platform powered by location intelligence that tracks who enters and exits a space, including the time and location. It utilizes facial detection technology, making it suitable for both workplaces and residential buildings, and allows for remote surveillance. The application is impressive because it can effectively recognize faces even when individuals are wearing masks or are beyond certain age restrictions.

Mastercard  —  Face recognized payment mode

The popular name in the payment industry, Matercard, uses face detection technology in their pilot program “smile-to-pay” to increase merchants as well as consumer experience. Customers need only to wave or smile in front of the camera to process the payment, making the task accomplished within a fraction of a second. Also, as the software only verifies people whose images are already present in the database, security remains at its best.

Clash of Streamers — Top-grossing mobile NFT game

With over 4 million installs on Android and iOS, Clash of Streamers stands as the world’s leading blockchain-powered mobile game and the highest-grossing in its category. A core feature is its ability to make characters uniquely personal by using player selfies. This is achieved through selfie segmentation and AR masks, both of which rely on an initial face detection stage to bring the characters to life.

Use Lystface API to Implement Smart Face Detection that Works for You! 

You’ve found tip-to-toe face detection algorithms and most importantly various applications that you can think of.

So, when face detection is catering all over the business verticals, why not integrate it? After all, your business should also be updated with this techies potential and benefits.

Keeping that in mind, we provide you with the Lystface API. And, why should you watch out for it?

It provides detailed API documentation that breaks down its integration process and hardly eats your excessive busiest minutes. Also, it comes with easy navigation, the highest security, and a free trial to test whether it suits your requirements.

So, if you want to create a shield against fraudsters without compromising it, you must Sign up with Lystface API!