Case Study – Provide thousands of videos, 3 per person, to create a full dataset for AI training
Thousands of Clickworkers from various continents send numerous videos, in which their face is clearly visible. With these video sets, an international software company is now able to train an AI system capable of clearly recognizing and identifying faces of all ethnicities and genders. The system can be used to perform a 3D-authentication security check for companies on the Internet to prevent fraud.
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The use of automated video recognition techniques, such as facial recognition, is becoming increasingly prevalent in various industries for combatting fraud. These techniques can be used to verify the identity of individuals in financial transactions, detect fraudulent behavior in security footage, and more.
However, training a high-quality video recognition system can be challenging due to the vast variability in people’s physical appearances and expressions.
The system must be able to accurately recognize and track the unique features of everyone’s face and adapt to changes in their appearance due to emotions or different angles. This requires a large and diverse dataset of videos for training the system. Our video recognition training data aims to provide this necessary resource for developing effective fraud detection systems.
To effectively combat fraud using video recognition 3D-Authentication techniques, a high-quality and diverse dataset is necessary for training the system. Our solution involves utilizing the crowd of thousands of individuals from various countries and regions, known as Clickworkers, to create videos of their faces. Following instructions, each Clickworker records a video of themselves three times, from different angles and with various facial expressions.
The Clickworkers upload their videos to the customer platform, along with information about the respective angles and facial expressions. All valid videos are then provided to us via an API connection. Then, our quality management team checks all the videos and their specifications. This process allows for the efficient and quick collection of a large and diverse dataset of videos for training video recognition systems.
More about our service “Video Recognition Datasets“
In conclusion, the use of software to perform automated 3D-authentication security checks is a promising approach to prevent online fraud. By utilizing 3D modeling and facial recognition technology, this solution can accurately and efficiently verify the identity of users, making it a valuable tool for organizations seeking to improve their security posture. In this case study, a institution was able to implement this technology and significantly reduce the incidence of online fraud.
Video recognition technology is being used in various sectors to combat fraud, including:
Banking and Finance: Video recognition is used to prevent card skimming, identity theft, and other fraudulent activities in banking and financial institutions.
Healthcare: Healthcare providers use video recognition to prevent medical identity theft and insurance fraud.
Retail: Retailers use video recognition to identify fraudulent activities such as return fraud, shoplifting, and credit card fraud.
Law Enforcement: Law enforcement agencies use video recognition to identify suspects in criminal investigations and prevent identity theft.
Overall, video recognition technology has become a critical tool for fraud prevention across various industries. As cybercriminals continue to develop new and sophisticated methods for committing fraud, businesses and organizations will need to stay ahead of them by leveraging innovative technologies like video recognition.
