How to Improve Facial Recognition Accuracy with AI

Typically, automated face recognition algorithms aim to mimic the way humans recognize faces. However, the human brain has the remarkable capability to store all the necessary visual data and retrieve it as needed. In contrast, the process is far more challenging for computers.

To identify a human face, an automated system requires access to a comprehensive database and must query it to match the observed features. The traditional approach has enabled the development of face recognition software that has performed reasonably well in many applications. However, this traditional method has largely reached its limits and doesn’t readily accommodate very large datasets of face data. This is where AI can help you solve this challenge.

What Factors Affect the Accuracy of Facial Recognition?

Numerous factors can impede accurate face recognition, including poor lighting conditions, rapid and sharp movements, varying poses and angles, and a range of facial expressions that may reflect a person's emotional state.

It is relatively straightforward to accurately recognize a frontal image that is evenly lit and captured against a neutral background. However, real-world situations are far more complex. The success of recognition can be compromised by changes in appearance, such as hairstyle, hair color, the use of cosmetics and makeup, and the effects of plastic surgery. The presence of items like hats, headbands, and the like can also play a role.

The key to achieving reliable face recognition lies in developing an AI-powered model with an efficient architecture, trained on as extensive a dataset as possible. This approach helps mitigate the influence of extraneous factors on the accuracy of image analysis.

Ways to Improve Face Recognition Accuracy With Deep Learning

Deep Learning is part of ML and is based on the use of artificial neural networks. The main difference between Deep learning and other machine learning methods is representation learning which doesn’t require specialized algorithms for each specific task.

Among the ways of improving neural networks for face recognition systems, it is appropriate to mention the following:

1. Knowledge distillation. A combination of two similar networks of different sizes, where the larger one trains the smaller one. As a result of training, a smaller network gives the same result as a large one, but it does it faster.
2. Transfer learning. This process is focused on training the entire network or its specific layers on a specific set of training data. This creates the possibility of eliminating bottlenecks. This creates the possibility of eliminating bottlenecks. For example, we can improve accuracy by using a set of images of exactly the type which cause errors to occur most often.
3. Quantization. This approach aims to speed up processing by reducing the number of calculations and the amount of memory used. Approximations of floating-point numbers by low-bit numbers help in this.

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