AI facial recognition systems are increasingly being used to leverage the uniqueness of a face as a biometric factor for identity verification and authentication in online login processes. The growing use of biometric facial recognition for the purpose of authentication is primarily based on the fact that, unlike traditional solutions such as verification emails, passwords, fingerprints or even simple selfies, it uses unique mathematical and dynamic patterns. As a result, it is one of the most reliable authentication systems.
The algorithms on which these AI systems are based must be trained with an enormous amount of data in the form of photographs and/or videos of people, until they are able to identify people unambiguously and without error on the basis of their faces using a machine learning process.
During the learning process, a multilayer neural network is used to process the training data. This network adjusts its face recognition parameters until a person can be clearly identified. This learning process requires not only large amounts of photographs and videos of people, but also a wide variety of people depicted, corresponding to the diversity of people in the regions where the system will be deployed. In addition, in order to train a biometric face recognition system, the training data must consist of photos of people whose faces can be seen in different sizes and from various perspectives and angles. When training the system, one must also keep in mind that when it is used to authenticate people, it must be able to recognize a face at all times, even if the face changes naturally over the years, sometimes to a greater or lesser extent.
We set up a custom-fit project on our in-house online platform in close consultation with the customer. This results in paid jobs/tasks for our registered crowdworkers, so-called Clickworkers. These tasks are made visible for 850,000 Clickworkers who match the demographics specified by the client.
Thousands of Clickworkers work on the project in accordance with the instructions obtained from the concise and descriptive task briefing. After specifying their ethnicity, the first step for each Clickworker who has accepted the task involves creating two new, short videos of themselves. In this case, they film their face, 1x with and 1x without glasses. While doing so, they slowly move their head in all directions and say a short sentence.
In the second step, each of the Clickworkers upload these two videos, as well as 60 to 200 existing digital photos of themselves — where their face is clearly recognizable — as a set to our platform. None of the photos from the set were taken on the same day, no photo is repeated, and covered in total a time period of min. 5 years to max. 20 years. The photos differ in terms of perspective or angle from which the person’s face is seen, styling (e.g. hairstyle, clothing, glasses, makeup), facial expression, and lighting conditions.
To ensure the correct implementation of the specifications, all the uploaded videos and photos are checked by our quality management team and selected accordingly. After being checked, the flawless sets are then transferred to the customer directly via an API connection.
This provides the software developer with over 300,000 photos of faces and over 6,000 high diversity videos promptly and efficiently. The software company uses this data as training data to train an AI system to clearly recognize faces until the error rate approaches zero and the system can be used for secure online authentication.
More information about our “Photo datasets for training facial recognition software” service