Face recognition technology

Exploring the Evolution of Face Recognition: From Eigenfaces to Deep Learning

Building a Face Recogniser: Traditional Methods vs Deep Learning

Over the past few decades, facial recognition technology has transformed from being prohibitively inefficient to an indispensable tool used by social media platforms, security departments, and smartphone companies around the world. The influx of deep learning within this domain has sky-rocketed the efficiency and use-cases of face recogniser apps and tools.

Here’s a look at how the methodology of building a face recogniser has evolved over the years.

Traditional face recognition: Eigenfaces

In the 1980s and 90s, the inception of Eigenfaces marked the first significant step in face recognition technology. These were blurry face-like images composed by superimposing various images onto each other pixel by pixel. The motive was to recognise unknown faces by associating them with probable Eigenfaces.

However, this method wasn’t without its flaws. Shifting a face image a few pixels could lead to misrecognitions, rendering the approach inefficient.

The next generation: Facial feature points

To overcome the shortcomings of the Eigenface method, facial feature points were introduced. This approach identifies pivotal points on a face — like the corner of the mouth or an eyebrow — and uses their coordinates in comparison with other faces after adjusting for slight off alignments.

This method, although better than Eigenfaces, didn’t utilise all available information such as hair colour, eye colour, and facial structures not captured by feature points.

You can find more details about the feature points method here.

The Deep Learning Revolution

The advent of deep learning and convolutional neural networks (CNNs) marked a paradigm shift in facial recognition technology. Using CNNs, a stencil-like structure repetitively walks over an image, identifying subsections that match specific patterns.

Initially, the patterns identified are simple edges and corners. But, as the process is repeated, higher-level features like parts of an eye or an ear emerge, eventually leading to the recognition of a whole face.

The advantage of this approach is that the patterns are not predefined but derived from training the network with millions of face images.

However, one of the challenges in developing a CNN-based face recogniser is the need for millions of images. A majority of developers rely on gathering images from the internet, but significantly more data can be collected when users willingly provide their personal photos. This explains why Facebook, Google, and Microsoft have impressively accurate face recognisers.

Road Ahead

Although deep learning has drastically improved face recognition technology, it has its limitations. Many companies utilise additional systems to correct for pose and lighting, often using a 3D mesh model of the face.

Machine learning-based facial recognition models are rapidly advancing, and we see impressive improvements year after year. For companies and brands who wish to incorporate the benefits of this blossoming technology, Fast Data Science, a leader in NLP, ML, and data science consultancy since 2016, offer world-class machine learning consultancy sessions.

Want to delve deeper into the world of face recognition? Explore here to learn more about building a face recogniser using traditional methods and deep learning.