Online face recognition: Face recognition with the help of AI

December 12, 2022 October 15, 2023

Identifying a face in images and videos is a standard task for artificial intelligence systems. Algorithms for online face recognition are trained based on millions of images obtained from the Internet and achieve increasingly reliable results. This not only applies to the assignment of image data to a specific person, but also to facial mood recognition and other relevant information for a wide range of applications.

Table of Contents

• Introduction- facial recognition

• How does online face recognition work?

• Areas of application for online face recognition

• Face recognition optimization

• Is online face recognition reliable?

• Difference between face identifier and face recognition

• Summary

• FAQs on Online Face Recognition

Introduction – facial recognition

Facial recognition, employs technology to visually map a person’s face and compare it to a database of recognized faces. Although this technology is widely used to confirm identities for personal purposes, its privacy consequences are still being investigated.

The usage of facial recognition technology is widespread and has many diverse applications. Smartphones with facial recognition technology as a way to access the device have been made available by companies like Apple, Samsung, and others. Face recognition technology is used by Facebook and Google to automatically “tag” photographs and generate links. Facial recognition is a tool used by law enforcement to find offenders. Facial recognition is increasingly being used in both private and professional settings.

How does online face recognition work?

The human face is not static, it changes constantly. This variability of appearance is the most challenging aspect of automatic face recognition. In addition, some images are taken from different positions under constantly changing lighting conditions. People also have different hairstyles at different times – they wear makeup, glasses or hats.

An online face recognition program must therefore apply filters to identify the key recognition features. With deep learning, systems learn to make these distinctions on their own with the help of large amounts of data from the Internet. To achieve this, the systems work primarily with the recognition of patterns. The more data used to train them, and the higher quality that data is, the better the results of the AI algorithms.

Tip:

Thousands of Clickworkers create image datasets for the training of online face recognition algorithms.

Get more information about AI Training Datasets

Areas of application for online face recognition

There are numerous areas of application for online face recognition:

• Automated annotation of image files with meta-data for the systematic sorting of large data sets.
• Face recognition for access-sensitive areas.
• Authentication of mobile devices with the built-in camera.

A further example of the usefulness of face recognition and face identifier are mobile banking tools. In developing countries, the Aella Credit finance company provides simple identity verification for customers which functions without human intervention. This opens up the financial market to groups of people for whom it was previously inaccessible.

Online facial recognition is also a valid method for sensitive areas with access restrictions. Not only can existing systems compare image data of people with access rights, but in some cases they can also predict intentions based on typical features. AI systems for online face recognition can identify

• Eyes being open or closed,
• Visual geometry of a face,
• State of mind,
• Approximate age of an individual.

Informative video on applications of online face recognition

Face recognition optimization

Image recognition is now the main area of application for AI training in neural networks. Here, facial recognition plays a special role. Given the issues that artificial intelligence already has in classifying simple objects, it is clear that matching faces, which are often only minimally different, is particularly difficult. Furthermore, AI systems are susceptible to attack by cybercriminals. Even small changes to images can be used to provoke false results.

However, there are several ways to address these issues:

• Explainable AI exposes the structures that neural networks use as discriminators. This allows the rapid correction of potentially erroneous internal operations.
• Images specifically designed for cyber attacks are already being used in the training of AI systems. This helps detect corrupted image files more quickly.
• Training images should show faces from as many different positions as possible and also in unusual situations.

Many companies use the possibilities of crowdworking to create image files for training purposes. This provides them with customized AI training data set files that are tailored to their purposes within a short period of time.

Is online face recognition reliable?

The result of online face recognition is a probability value. Matching a person with an image is therefore only more or less reliable. However, the advantage of a machine-based similarity comparison is not only the fact that a program will usually provide the probability value at the same time. Research has shown that modern face identifier technologies are superior to the human eye. Google’s neural network Face Net, for example, achieves a hit rate of over 99 percent.

Various features determine the individuality of a face. These include the geometry of a face – i.e. the relationship between the eyes, the nose, the forehead and the mouth. A system designed to highlight similarities between a specific image and existing image data works with these key facial elements. The information regarding a person’s identity is superfluous as long as only similarities are searched for.

It is clear that automatic systems always provide only a probability value regarding matches, similarities or differences. Depending on the areas of application, the user of the program can then select a similarity threshold that is sufficient for the purpose at hand. In the public safety sector, a threshold of 99 percent is preferred, while a simple online image search is practical with a match probability as low as 95 percent.

Difference Between Face Identifier and Face Recognition

Facial technology has seen rapid advancements over the years, making the process of distinguishing between terms like “Face Identifier” and “Face Recognition” vital. While both are grounded in the science of analyzing facial features, their objectives and implementations differ. This chapter delves into these distinctions.

Definition

Before diving deeper into their differences, it’s essential to understand what each term means at its core.

• Face Identifier: This refers to a system or algorithm that assigns a unique identifier to each distinct face it detects. Essentially, it ‘labels’ or ‘tags’ faces but does not necessarily match them to known identities. Its main task is to differentiate one face from another.
• Face Recognition: This is a broader concept that encompasses the detection, analysis, and identification of faces against a database. It matches a detected face to a known identity in its database, thus ‘recognizing’ who the individual is.

Technological Underpinnings

The technological backbone of these systems varies based on their core functionalities. Let’s explore the algorithms and mechanisms that drive them.

• Face Identifier
• Primarily relies on feature extraction to map unique facial features and assign a unique ID.
• Does not require a vast database of known faces, but rather a dynamic system that can generate and track unique identifiers.
• Face Recognition
• Employs deep learning algorithms, especially convolutional neural networks (CNNs), to detect and match facial patterns.
• Requires a database of known faces and their respective identities.

Privacy Implications

In today’s digital age, privacy concerns surrounding facial technologies have taken center stage. Understanding the privacy implications of each system is crucial for ethical deployment.

• Face Identifier
• Generally considered less intrusive, as it doesn’t tie facial data to real-world identities.
• Anonymizes data, which can be preferable in settings where user privacy is paramount.
• Face Recognition
• Raises more privacy concerns, given its ability to pinpoint identities.
• Usage often requires more stringent regulations and user consent.

Accuracy and Challenges

No technology is without its hurdles. The efficacy of these facial systems, and the challenges they face, offer insight into their real-world applications.

• Face Identifier
• Main challenge lies in ensuring that the system consistently assigns the same identifier to an individual across various conditions.
• Face Recognition
• Accuracy can be impacted by factors like lighting, pose, and occlusions.
• Has been under scrutiny for potential biases, especially in recognizing diverse faces across races, ages, and genders.

Summary – online face recognition

Online face recognition using artificial intelligence offers numerous potential applications. However, the danger lies in using these automatic methods as an autonomous tool. Whenever personality rights are affected, additional human analysis must therefore be granted. In most areas, however, online face recognition is a powerful tool designed to make tasks easier and more efficient.

• Explainable AI exposes the structures that neural networks use as discriminators. This allows the rapid correction of potentially erroneous internal operations.
• Images specifically designed for cyber attacks are already being used in the training of AI systems. This helps detect corrupted image files more quickly.
• Training images should show faces from as many different positions as possible and also in unusual situations.

Robert Koch