Leading Face Biometric Technology Manufacturers Transforming Automotive Security
Advanced face biometric technology enhancing security in a modern vehicle dashboard display.
Biometric identification is ushering in a new era of enhanced safety and security for electric vehicles (EVs). This technology, including systems from prominent Face Biometric Technology Manufacturer For Automotive applications, is pivotal as the industry navigates significant transformations. EVs stand at the convergence of electrification, the rise of software-defined vehicles, and the integration of biometrics, alongside shifting socio-economic attitudes towards car ownership influenced by affordability and environmental concerns. While biometrics alone won’t solve all socio-economic issues, their correct application promises positive impacts, initially focusing on security, safety, and convenience. For instance, reliable biometric identity assurance could underpin peer-to-peer vehicle rentals and enable secure, hands-free e-commerce transactions within the car.
Experts from global biometrics specialists, Tier 1 automotive suppliers focusing on camera-based facial authentication, and technology corporations bridging physical and digital worlds highlight the core benefits: a seamless customer experience, improved safety, security, cybersecurity, and enhanced in-car leisure. Key application areas include vehicle security, Advanced Driver Assistance System (ADAS) functions like fatigue detection via behavioral biometrics, health monitoring, and infotainment systems.
Understanding Biometrics in Vehicles
Biometrics involves measuring unique characteristics of living beings. Computers can interpret sensor readings of these traits to identify individuals with high confidence or assess their fitness to drive. These characteristics fall into two categories: physiological and behavioural.
Physiological biometrics include face recognition, voice patterns, fingerprints, iris and retinal scans, vein patterns, and hand geometry. Some traits like heartbeat, respiration rate, blink rate, facial expressions, and speech patterns can indicate physiological issues affecting driving ability, while also potentially serving identification purposes.
Behavioural biometrics complement physical measurements. They help confirm identity and detect driver impairment due to tiredness, illness, or intoxication by analyzing characteristic patterns in actions like steering, braking, or even interacting with infotainment systems.
The Synergy of EVs and Biometrics
There’s no inherent quality in EVs or hybrids that makes biometrics exclusively essential for them compared to internal combustion engine (IC-engined) vehicles. However, the paradigm shift represented by EVs and their supporting infrastructure encourages upfront investment in advanced capabilities like biometrics, according to a digital identity specialist.
“While the benefits apply across the board, the practicalities of implementing them are encouraged by the migration to EVs and the software platforms that support software-defined vehicles,” he notes. Electrification, developing alongside the software definition of vehicle functions, presents a prime opportunity to integrate these technologies early, fostering innovation.
“If I am designing a car, I can imagine it being like a large smartphone that also transports people. Its software will be updated on-the-fly, and I can put new capabilities in. I am automatically looking at this platform very differently and allowing the innovation hubs to be embedded into it.”
An expert from a multi-industry corporation adds that electrification accelerates the rise of the software-defined, digital vehicle. “In this respect, biometric technology will be implemented more in the EV ecosystem, not only on the car itself, but in the charging experience as well.” Biometric authentication could streamline mobile in-car payments at charging stations. A Tier 1 supplier expert mentions, “Those technologies simplify enormously the process for installing and releasing charging apps from OEM app stores. Another use case is checking drivers’ licences for shared or rented vehicles.”
Key Biometric Technologies in Automotive
Currently, fingerprint readers and facial recognition systems dominate automotive applications, primarily for access control, with iris scanners following. These systems require sensors, processing power, and a robust back-end data management infrastructure.
A multi-industry corporation expert explains their role in bridging the physical and digital realms by capturing, encrypting, and managing personal biometric data, verifying identity by comparing live data with stored records. “The combination of these two types of expertise enables successful management of any kind of ID check, enrolment and access management, both physical and logical,” he states.
The high security against spoofing is a major benefit, preventing cybercriminals from stealing user identities, notes the Tier 1 supplier expert. “Another is secure, convenient and fast authentication of users,” he adds. “For example, manual entry of a long and complex pin code is no longer necessary, and no further devices are needed for authentication.”
Further security enhancements are anticipated. A biometrics expert envisions preventing car-jacking: “Imagine if the car was disabled because the unknown intruder was trying to drive it away – it just eliminates that risk.”
Driver using fingerprint scanner integrated into car start button for biometric vehicle access.
Biometric authentication also offers convenience for authorised drivers. “I see a world where I can lend my car to my son while setting a 2 hour time window… but not to go more than 25 miles… There are all sorts of things that you can do if the key is biometric-based that a physical key does not enable.”
Maturing Technologies: Fingerprint, Face, and Iris
Iris scanning, fingerprint reading, and face identification are the most mature general biometric technologies, with the latter two significantly boosted by smartphone integration, notes the Tier 1 supplier. Their maturity has accelerated recently due to AI and machine learning (ML). “The impact of ML in terms of the accuracy of these algorithms has been enormous; they have come on leaps and bounds, multiple orders of magnitude in the past 5 years alone,” says the biometrics specialist.
However, maturity varies by application and environment. “It’s really a matter of what is most suitable and most mature for the purpose. I would argue that for outside the car, fingerprint or face recognition is best, while inside the car it is situational, and face and voice recognition each has certain advantages.”
Tier 1 suppliers, including established Face Biometric Technology Manufacturer For Automotive solutions, have developed secure facial authentication capabilities integrated into driver identification displays, often adding to existing driver monitoring systems. As monitoring cameras constantly observe the driver, facial recognition combined with voice recognition provides multi-factor authentication for secure e-commerce transactions. “You can say I would like to pay my latest Amazon order. Your voice can be used to authenticate that, and you don’t have to take your hands off the wheel or your eyes off the road,” explains the biometrics specialist.
Voice is generally considered a secondary authentication factor due to challenges like accommodating diverse languages, accents, in-car noise, and microphone quality variations.
Reliability of any automated identification technology hinges on two metrics: the false match rate (FMR) and the false non-match rate (FNMR). “The false match rate is a probabilistic indicator of the chance that you can impersonate me,” the expert explains. “The false non-match rate is a probabilistic indicator of the likelihood of the system rejecting me, even though I am the right person.” These rates often have an inverse relationship: improving accuracy (lowering FMR) might slightly increase the chance of rejecting the correct user (higher FNMR), though advancements in AI/ML are minimizing this trade-off.
Fingerprint Recognition Details
Automated fingerprint recognition, refined over decades, is relatively new to automotive use. Fingerprints feature unique patterns of ridges, valleys, and minutiae (ridge endings, bifurcations). Sensors capture these patterns using optical, ultrasonic, thermal, or capacitive methods, often placed on door handles or start buttons.
Optical sensors were common but vulnerable to spoofing. Capacitive sensors, measuring capacitance differences between skin ridges and air-filled valleys, or hybrid systems, are now prevalent, especially after widespread use in mobile phones.
Hyundai pioneered integrated fingerprint scanning in its 2019 Santa Fe SUV, using a capacitive reader to unlock doors and start the engine. Hyundai claims a misidentification rate of 1 in 50,000, significantly more secure than conventional keys, some of which are susceptible to relay attacks.
Hyundai Santa Fe hybrid SUV featuring integrated fingerprint biometric security system.
Focus on Facial Recognition
Automated face recognition development dates back to the 1960s, but only became practical for consumer electronics like smartphones in the 2010s with affordable, powerful processing. This software-intensive technology uses cameras and algorithms to analyze facial geometry – measuring distances between eyes, forehead-to-chin length, and pinpointing landmarks like nose, eyes, and mouth corners to create a unique mathematical facial signature.
Convolutional neural networks compare this live signature against a database of known faces or the enrolled user’s signature stored in the vehicle’s security system. Leading face biometric technology manufacturer for automotive applications focus heavily on refining these algorithms.
Historically, accuracy varied based on race, age, and sex, but significant R&D is addressing these biases. User cooperation generally improves accuracy. The US National Institute of Standards and Technology (NIST) benchmarks facial recognition algorithms. As of April 2020, the top algorithm tested by NIST achieved an error rate of just 0.08%, a dramatic improvement from the 4.1% error rate of the best algorithm in 2014.
Smartphone displaying facial recognition interface, highlighting technology transfer to automotive biometrics.
Iris Scanning Advantages
Iris scanning offers inherent advantages for identification. Despite the iris’s small size (average 11 mm diameter), the pattern variation between individuals is immense, patterns differ between left and right eyes, the iris is well-protected environmentally, and changes little over a lifetime. Distinctive features include arching ligaments, furrows, ridges, crypts, rings, corona, freckles, and a zigzag collarette. Near-infrared (NIR) light reveals deeper stromal fibre patterns.
Iris recognition is relatively robust against varying illumination and viewing angles. Systems typically locate the pupil, iris, and eyelids, isolate the iris image, divide it into blocks, convert these into feature values, and compare them against enrolled scans. The rear-view mirror is a common location for NIR camera systems. The vast individual variation makes iris scanning arguably the most secure biometric, with one developer claiming a false match rate of 1 in 10 million.
Rear-view mirror housing an iris scanning camera for secure automotive biometric identification.
Behavioral Biometrics: How You Drive Matters
While physiological biometrics measure what people are, behavioural biometrics measure how they act, often unconsciously. Characteristic ways of moving, speaking, gesturing, typing, or operating vehicle controls can form a biometric signature.
“These days, in financial services, we use behavioural biometrics to do continuous authentication,” says the biometrics specialist. “How you fill out a password… how you hold your phone and swipe… are behavioural characteristics. You can combine a lot of these signals… to get a very accurate picture.”
This is crucial for future automotive applications, contributing not only to authentication but also to assessing driver attention and health. “Take driver fatigue for example, facial biometrics can identify that it’s the right driver,” he says, “but behavioural biometrics might tell you he’s falling asleep or that he is driving slightly erratically… hitting the accelerator and the brake when there’s no traffic.”
Advanced Monitoring: Heart Rate at the Wheel
Electrocardiogram (ECG) data can also serve identification, authentication, and health monitoring purposes. Sensors integrated into steering wheels allow continuous, unobtrusive driver monitoring. Development began in the early 2010s. A 2015 Portuguese project, CardioWheel, used conductive fabric electrodes linked to signal processing units to measure heart rate and identify drivers based on ECG patterns, despite challenges from hand movements.
More recently, a German research team reported using flexible printed polyurethane electrodes with silver conductors on steering wheels, achieving better signal quality. Testing across rest, city, highway, and rural driving scenarios showed the system provided usable ECG signals over 45% of drive time, demonstrating feasibility.
The Power of Combining Biometrics
Combining multiple biometric modalities enhances security, safety, and convenience. A biometrics specialist argues that combining face, voice, and behavioural biometrics holds significant potential, particularly applying behavioural analysis to facial cues.
Driver monitoring system camera view tracking eye movement for drowsiness detection using behavioral biometrics.
“While your face won’t change if you’re tired, how you present it will,” he explains. “I can detect if your eyes are closing or your head is dipping… if your blink rate deviates…” Combining this ‘face-behavioural’ data with voice analysis adds capabilities like detecting stress, duress (via codewords), or impairment from alcohol, drugs, or medication side effects (“slurring their speech”).
The Tier 1 supplier expert notes that other biometrics like body size and weight can optimize airbag deployment, adjust seats for comfort, and personalize climate control settings. Capturing biometric data unobtrusively while the subject is moving is key for seamless operation, highlights the multi-industry corporation expert, citing smartphone face recognition as a successful example.
The Role of AI and Machine Learning
AI, especially ML, is indispensable for interpreting biometric data. The multi-industry company expert emphasizes AI’s role in improving performance, calling it the simplest technology for complex problems. The Tier 1 supplier expert notes, “AI is needed to train the software algorithm during the development phase. Once loaded in a final product, the software doesn’t need the ML anymore.”
Reliability depends heavily on the quantity and diversity of training data. “This is the biggest challenge,” he says. “The bigger the training network the more reliable the results… investment in a large AI network… leads to a more robust system… with almost no false-positive results.” Models are rigorously assessed against large datasets.
“Training is a big piece of it,” agrees the biometrics specialist. “‘Garbage in, garbage out’ was never more true than in biometrics… one of the things we are always very conscious of is ground truth in the data.” He warns about future hacking targeting data integrity: “Hacking in the future won’t be about code, it will be about data… inserting bad data into good… That’s how hackers will attack ML systems – with adversarial AI.” Continuous learning is vital, allowing systems like advanced face recognition to adapt over time. “The power of ML is critical; without it the algorithm is static.”
Implementation Hurdles and Solutions
Vehicle electronic architectures are shifting towards centralized computing platforms, which aids biometric integration. However, challenges remain. “Biometrics vendors will need to adapt their offerings to… the vehicle market and its specifics,” notes the multi-industry corporation expert. While security is a primary driver, new use cases like ADAS, fatigue detection, and health monitoring require focus.
Sensor technology issues, like varying light levels for cameras or ambient noise for microphones, need addressing. Despite this, biometric technologies have become more resilient. “They have got better at filtering out bad signals as they continuously improve through machine learning,” states the specialist, noting advancements in handling background noise for voice recognition and light interference (like glare) for cameras, partly due to broader spectral responses beyond visible light.
High-performance cameras and microphones are now more affordable and often serve multiple purposes. Many new vehicles already include cameras and microphones for non-biometric functions. “If you look at Teslas, there are cameras all over them… all you are doing is taking a feed from one or more of them and running it through the software,” says the biometrics specialist. “All those cars also have Bluetooth for voice calls and, as they are software-defined vehicles, you could push an update into them tomorrow that does voice and face biometric identification, and no additional hardware would be needed.”
The Tier 1 supplier confirms their secure face authentication reuses existing driver monitoring hardware with minimal additions, ensuring compact and simple integration. On the software front, while the industry initially relied on platforms like Apple CarPlay and Android Auto, there’s a growing realization among OEMs of the need for proprietary platforms integrating these advanced features.
Conclusion: The Future is Identified
Biometrics in vehicles, particularly EVs, are just beginning to demonstrate their transformative potential. Solutions from leading face biometric technology manufacturer for automotive companies are becoming increasingly integrated. Embedding these capabilities into core computing platforms will make them more accessible to OEMs, enabling secure access to a multitude of networked services. As vehicles evolve beyond mere transportation devices, knowing the verified identity of the occupants becomes fundamental to unlocking new functionalities and ensuring a secure, personalized experience. The integration of robust biometric systems is central to this automotive future.
Some suppliers of automotive biometrics systems
Facial recognition
BioEnable Technologies
+91 20 6560 0600
www.bioenabletech.com
Precise Biometrics
+1 315 274 9444
www.precisebiometrics.com
Tier 1 supplier
Continental
+1 248 393 5300
www.continental.com
Daon
+1 703 984 4000
www.daon.com
Thales
+33 1 57 77 80 00
www.thalesgroup.com
Iris recognition
Biomatiques
+91 261 2255767
www.biomatiques.com
EyeLock
+1 855 393 5625
www.eyelock.com
IDEX Biometrics
+1 339 215 8020
www.idexbiometrics.com
Iris Guard
+44 1908 991683
www.irisguard.com
IriTech
+1 703 877 2135
www.iritech.com
Voice recognition
Nuance
+44 1628 491600
www.nuance.com
Sensory Inc
+1 408 625 3300
www.sensory.com
Fingerprint recognition
Bayometric
+1 408 940 3955
www.bayometric.com
Fingerprint cards
+46 10 172 00 00
www.fingerprints.com
Next Biometrics
+47 22 70 00 95
www.nextbiometrics.com
Synaptics
+86 10 8477 7300
www.synaptics.com
