The future of the automotive industry hinges on autonomous and connected vehicles, leading to advancements in connectivity, safety, efficiency, and user experience technologies. In the areas of safety and user experience, companies have made growth in developing biometric systems to accurately authenticate users and monitor driver alertness, among other capabilities.
Various new applications have arisen for integrating multimodal biometric systems, including in-cabin monitoring for user authentication and car-sharing features. According to the Trend Report for Mobility by IDnow, these systems are increasingly being utilized in use cases such as car rental, car financing, micromobility, and mobility-as-a-service to meet stringent KYC requirements through face biometrics for identity verification.
A company blog post notes that connected cars either have integrated 4G or 5G network connections, or work through the user’s smartphone. Benefits of connected vehicles enumerated in the post include predictive maintenance, personalized services, online entertainment and applications and remote operation.
IDnow has developed identity verification APIs with face biometrics and liveness detection powered by AI algorithms to authenticate customer identity documents and driver’s licenses with their phones. The objective is to ensure the legitimacy of identity documents by checking security features such as holograms, as well as enabling NFC functionality for chip-enabled identity documents.
In an application of biometric user verification in the mobility sector, IDnow partnered with TIER to enable the mobility company to digitally verify necessary documents from customers through a photo of their driver’s license. IDnow also formed an alliance with Citygo, a French short-distance urban carpooling company, to implement IDnow’s identity verification software for validating driver’s licenses and proof of address while adhering to KYC regulations.
Driver monitoring and assistance
In-car and in-cabin monitoring systems are finding new application space for facial recognition. A team of researchers from Edith Cowan University in Australia has designed a facial recognition model that utilizes standard in-car cameras, commonly used as dash cams, to detect signs of intoxication. The system has shown a 75 percent accuracy rate in estimating levels of blood alcohol by analyzing facial movements, gaze direction, and head position based on visual characteristics.
Researchers at Nanning University in China have developed a new method to enhance driver fatigue detection by utilizing facial recognition and emotion state analysis, even in environments with changing lighting conditions. The method employs deep convolutional neural networks (CNN) for extracting facial features and includes emotion state analysis for a comprehensive assessment of fatigue. The system attained a detection accuracy of 95.3 percent on the YawDDR dataset.
While promising advancements are being made in biometric systems for the automotive industry, the research is important in enhancing road safety and driver well-being through intelligent driving assistance systems. Cipia, a company specializing in computer vision for in-cabin applications, revealed that its Driver Sense, a driver monitoring system, has been chosen by a European sports car manufacturer.
Article: Biometrics key to controlling autonomous, software-defined vehicles