Refine
H-BRS Bibliography
- yes (38)
Departments, institutes and facilities
Document Type
- Conference Object (19)
- Article (12)
- Patent (3)
- Part of a Book (2)
- Report (2)
Year of publication
Keywords
- Skin detection (2)
- automated sensor-screening (2)
- biometrics (2)
- machine learning (2)
- optical sensor (2)
- semiconducting metal oxide gas sensor array (2)
- Circular saws (1)
- Collaborating industrial robots (1)
- Cybersecurity (1)
- Embedded system (1)
Persons entering the working range of industrial robots are exposed to a high risk of collision with moving parts of the system, potentially causing severe injuries. Conventional systems, which restrict the access to this area, range from walls and fences to light barriers and other vision based protective devices (VBPD). None of these systems allow to distinguish between humans and workpieces in a safe and reliable manner. In this work, a new approach is investigated, which uses an active near-infrared (NIR) camera system with advanced capabilities of skin detection to distinguish humans from workpieces based on characteristic spectral signatures. This approach allows to implement more intelligent muting processes and at the same time increases the safety of persons working close to the robots. The conceptual integration of such a camera system into a VBPD and the enhancement of person detection methods through skin detection are described and evaluated in this paper. Based upon this work, next steps could be the development of multimodal sensor systems to safeguard working ranges of collaborating robots using the described camera system.
Robust Identification and Segmentation of the Outer Skin Layers in Volumetric Fingerprint Data
(2022)
Despite the long history of fingerprint biometrics and its use to authenticate individuals, there are still some unsolved challenges with fingerprint acquisition and presentation attack detection (PAD). Currently available commercial fingerprint capture devices struggle with non-ideal skin conditions, including soft skin in infants. They are also susceptible to presentation attacks, which limits their applicability in unsupervised scenarios such as border control. Optical coherence tomography (OCT) could be a promising solution to these problems. In this work, we propose a digital signal processing chain for segmenting two complementary fingerprints from the same OCT fingertip scan: One fingerprint is captured as usual from the epidermis (“outer fingerprint”), whereas the other is taken from inside the skin, at the junction between the epidermis and the underlying dermis (“inner fingerprint”). The resulting 3D fingerprints are then converted to a conventional 2D grayscale representation from which minutiae points can be extracted using existing methods. Our approach is device-independent and has been proven to work with two different time domain OCT scanners. Using efficient GPGPU computing, it took less than a second to process an entire gigabyte of OCT data. To validate the results, we captured OCT fingerprints of 130 individual fingers and compared them with conventional 2D fingerprints of the same fingers. We found that both the outer and inner OCT fingerprints were backward compatible with conventional 2D fingerprints, with the inner fingerprint generally being less damaged and, therefore, more reliable.
Entering the work envelope of an industrial robot can lead to severe injury from collisions with moving parts of the system. Conventional safety mechanisms therefore mostly restrict access to the robot using physical barriers such as walls and fences or non-contact protective devices including light curtains and laser scanners. As none of these mechanisms applies to human-robot-collaboration (HRC), a concept in which human and machine complement one another by working hand in hand, there is a rising need for safe and reliable detection of human body parts amidst background clutter. For this application camera-based systems are typically well suited. Still, safety concerns remain, owing to possible detection failures caused by environmental occlusion, extraneous light or other adverse imaging conditions. While ultrasonic proximity sensing can provide physical diversity to the system, it does not yet allow to reliably distinguish relevant objects from background objects.This work investigates a new approach to detecting relevant objects and human body parts based on acoustic holography. The approach is experimentally validated using a low-cost application-specific ultrasonic sensor system created from micro-electromechanical systems (MEMS). The presented results show that this system far outperforms conventional proximity sensors in terms of lateral imaging resolution and thus allows for more intelligent muting processes without compromising the safety of people working close to the robot. Based upon this work, a next step could be the development of a multimodal sensor systems to safeguard workers who collaborate with robots using the described ultrasonic sensor system.
Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device’s modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements.