It is a process in which the system compares two or several images of an individual to identify whether they belong to the same person. For this purpose, the system focuses on a person’s facial features, including the distance between the eyes, mouth, and outline to verify whether it is the same person. Identifying people is useful in several areas such as maintaining security, smart watches, mobile phones, healthcare centers, social media, and retail. This process is completed and involves several steps that include detection, extraction of features, comparison, and final recognition.
Technologies Working Behind the Process
Face matching is not just all about detection, extraction, comparison, and identification but it is a complex process that involves several technologies to proceed further. Here are some of the technologies used in this process to make accurate decisions:
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Computer Vision
Liveness detection is a technique of artificial intelligence that enables computers to learn to read visual data such as images and videos. It is a training process for a computer system through which the system can interpret various features present in an image or a video. This process involves two basic steps: image interpretation and the detection of facial features.
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Machine Learning
When it comes to this particular process, the system is allowed to learn to identify people by comparing the images or faces from a large database of other images. It works by using two main methods that are deep learning, and classical approaches. Deep learning involves CNN (Convolutions Neural Networks). It is a neural network exactly like the one present in the brain. It automatically identifies people by comparing them with the large database. It can analyze minor facial features and can detect faces in any environmental condition.
On the other hand, classical approaches were used earlier. It contains two major processes, Eigenfaces and Fisherfaces. Eigenfaces analyze primary features that distinguish one person from another. While Fisherfaces analyzes the face based on several facial features like a jawline, and distances between eyes, and can also analyze the picture from any angle.
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3D Facial Recognition
This method analyzes a picture in 3D. Unlike, 2D analysis of the picture, this process makes the most out of it and provides all the possible interpretations of a face. No light and angles bother the face match. This method is most commonly used for security purposes.
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Multi-Modal System
Face matching system can also be combined with other biometric system to enhance security. It can be merged with any other system like fingerprint scan, iris scan, and others. It is useful when due to any technical issues, one system cannot identify the face so others will be active to recognize it.
Limitations While Using this Feature
Although it is a powerful tool it has several limitations that can affect the accuracy and authenticity of the system. Here are some of the major challenges faced during the matching process:
- AI face match can be affected by several factors, including age, health, and physical changes that can change a person’s facial features. Over time, these changes can make the verification process challenging.
- It deals with the differences in posture, lighting, and other environmental factors like darkness or inappropriate light. It is one of the most frequent problems faced by this technology.
- Age, gender, and race are the few instances of demo-demographic limitations. The technology becomes biased when it comes to detecting the people that belong to another race or ethnicity.
- The signs of aging may affect the process of identification. The signs may include wrinkles, saggy skin, the texture of the skin, and color changes as well. The image in the database should be updated timely to avoid any inconvenience.
- Some accessories can disturb the whole process and are unable to recognize faces.
- Low-quality images can also affect the process and can make unfair decisions.
Conclusion
Deep learning techniques, including deepfake detection, have replaced 2D matching in technology. Online face matching has also evolved over time, and many institutions now use it. It can be applied to stop scams and fraud. This advanced technology has the potential to be far more advanced and will probably surpass every obstacle. Several organizations’ security can be preserved by integrating such technology. However, not every decision given by the system needs to be correct, which can cause distrust in users. Additionally, remote workers might get verified with the use of online face matching.
