Bi-modal palm recognition is rapidly emerging as a frontrunner in access control, offering a secure and swift method for user verification, particularly in environments demanding the highest security standards.
While the concept of using palm features for identification isn’t new, the true potential lies in the technological sophistication required to unlock the wealth of “information” embedded within the human hand. Early palm readers, akin to fingerprint scanners, primarily captured surface details. Although the larger palm area provided more data, it essentially captured the same type of information without adding significant security layers.
Furthermore, these systems demanded precise hand placement, requiring users to position their palms identically to their initial profile creation. Similar to fingerprint sensors, these older readers were susceptible to errors caused by wear and tear on the palmprint, a common occurrence, especially in physically demanding workplaces.
Armatura’s ARMLivePalm 9.6 SDK Bi-Modal Palm Recognition Technology marks a significant advancement, seamlessly integrating visible-light palm shape analysis with near-infrared palm vein mapping. By simultaneously employing two verification modes, it surpasses previous generations in accuracy and presentation attack detection. Its enhanced speed and stability across varying environmental conditions unlock new and exciting applications for this technology.
The human palm offers two distinct layers of biometric data. The external layer comprises the palm’s visible geometry, including lines, texture, curvature, and finger spacing. The internal layer reveals subcutaneous vein patterns formed by the body’s vascular structure. Remarkably, these vein patterns are unique, even among identical twins, and remain stable over time due to their protection from wear or surface damage.
This dual-layered information makes the palm one of the richest biometric sources available, offering more diverse data points than fingerprints and greater resilience to aging or surface changes.
Cutting-edge performance engineered in the US
The ARMLivePalm 9.6 SDK Bi-Modal Palm Recognition Technology, an algorithmic platform developed in the US, is designed for seamless integration into various access control terminals, gates, and mobile devices, making it ideal for professional-grade security deployments.
ARMLivePalm 9.6 can complete full authentication cycles in under half a second. It supports databases of up to 100,000 users and maintains an exceptionally low false rejection rate—below 0.01 percent at a strict false acceptance rate of 0.001 percent. Most importantly, it sustains this level of precision across varied lighting conditions and hand positions.
A key focus during the development of ARMLivePalm 9.6 was enabling advanced hand pose tolerance. The goal was to allow users to move their palm naturally over the reader, eliminating the need for rigid alignment.
These enhancements distinguish ARMLivePalm 9.6 from previous palm or fingerprint systems, which often required compromises between speed, capacity, and security. Armatura’s latest technology demonstrates that large-scale, touchless palm recognition can now deliver enterprise-level accuracy while maintaining the convenience users expect in demanding environments such as corporate campuses, hospitals, or public venues.
Putting AI into practice
At the core of ARMLivePalm 9.6 lies a sophisticated AI backbone, enabling the system to capture and interpret the palm’s visible and invisible features in real time. The technology leverages two powerful machine learning concepts: transformer-based architecture and GAN-driven data simulation.
These elements enable the platform to operate more efficiently, adapt to diverse environments, and maintain high accuracy even in challenging conditions such as low light, motion, or partial occlusion.
Transformers, a type of neural network originally designed for natural language processing, analyze images by focusing on distinct structures and relationships between different elements, regardless of their proximity. This approach mirrors human contextual understanding, allowing for connections between seemingly disparate events.
Fueled by transformer-based AI, ARMLivePalm 9.6 simultaneously analyzes visible-light and near-infrared palm data, identifying the most reliable features—texture, curvature, or vein patterns—and correlating them to achieve optimal accuracy.
The training of the ARMLivePalm 9.6 algorithm utilizes Generative Adversarial Networks (GANs), which employ two competing neural networks: a generator that creates synthetic images and a discriminator that distinguishes real from fake. This process not only enhances accuracy but also increases the system’s tolerance to legitimate variations in image data. GAN training enables ARMLivePalm 9.6 to account for changes in skin tone, surface injuries, skin conditions, and varying lighting conditions, ensuring consistent accuracy.
Ease of deployment
ARMLivePalm 9.6 is designed for flexible adaptation to various operational environments. It can serve as the primary authentication method in high-security or privacy-sensitive settings, such as laboratories, data centers, or hospitals, where contactless identification and spoofing resistance are critical. Alternatively, it can function as a supporting layer in hybrid systems, combined with other biometric methods like facial recognition. This is particularly useful when convenience and redundancy are paramount, or when system operators seek the added security layer without a complete infrastructure and database overhaul.
ARMLivePalm 9.6 offers seamless integration across multiple platforms, available as an SDK running on Windows, Android, and embedded Linux systems. Its compact model footprint of less than 100mb allows for deployment on a wide range of hardware, from full-scale terminals to compact edge devices.
This cross-platform compatibility can reduce deployment costs by up to 40% for system integrators, as ARMLivePalm 9.6 unlocks palm recognition without the need for proprietary readers.
Scalability was another key focus during development. With APIs for C/C++, Java, and C#, it suits both single-entry systems and complex multi-site networks. Built-in diagnostic and sandbox tools enable integrators to test updates and monitor performance in real time, regardless of their architecture’s size and characteristics.
Beyond access control
ARMLivePalm 9.6 is designed to go beyond simple identity verification. Its open APIs allow integrators to connect the technology with other domains such as time attendance, visitor management, and transaction authorization. By synchronizing data with these systems, it streamlines operations in high-demand environments, from corporate offices and hospitals to factories and transportation hubs.
In environments where privacy is paramount and facial recognition is unsuitable or restricted, such as in Europe due to GDPR regulations, ARMLivePalm 9.6 provides organizations with biologically unique user data that is not prone to misuse, such as the creation of deepfakes. The platform is designed with security in mind: biometric data is encrypted end-to-end, and the system’s over-the-air update framework ensures that performance and anti-spoofing algorithms remain current against evolving threats.
The platform’s security is rigorously validated, meeting the ISO/IEC 30107-3 Level 2 standards for presentation attack detection, with a false rejection rate of 1.0–1.5%.
Armatura envisions bi-modal palm recognition, powered by the richness of data generated by ARMLivePalm 9.6, its ease of use, and its security features, as a key component in future cognitive ecosystems. ARMLivePalm 9.6 unlocks new possibilities for organizations, bridging physical identity and digital intelligence in ways that redefine the capabilities of access control, from access control terminals to connected workplaces and smart building infrastructures.
For more information about Armatura, please visit https://www.armatura.us.