Neurotechnology

In response to market demands, since 2000 we have also been involved in developing a range of products for smartcard-based biometrics and embedded applications, as well as some end-user products.

In 2004, to better accommodate the growing volumes of research in artificial intelligence, the company founded its robotics division, which began research in the field of mobile autonomous robots.

In 2009, to assist our customers in selecting suitable hardware for their implementations of biometric systems, we started Biometric Supply. This subsidiary offers a wide range of fingerprint readers and iris scanners from multiple manufacturers.

The year 2012 was rich with events for Neurotechnology. To take advantage of the new opportunities brought by rapid growth of cloud technologies, we started SkyBiometry. This subsidiary now provides face detection and recognition software as a cloud-based service. Also in 2012, a strategic decision was made to start a division in Sri Lanka. The team of developers there is now the prime force in developing our biometric solutions for attendance systems.

In 2014, Neurotechnology released SentiBotics, a ready-to-use robotics development kit. The same year, the company established the Ultrasound Research Group to undertake research in the fields of ultrasonic particle manipulation, parametric array and transducer technology.

In almost 30 years of our activity, we have accumulated substantial experience in the area of neural networks. This allowed us to develop a multitude of product capabilities based on deep learning as well as customer applications.

Deep neural networks are able to solve many problems - such as image classification, object detection, or instance segmentation - more efficiently than traditional computer vision algorithms. To facilitate this process, we have built a technology that has tools to support developing AI-based object recognition applications.

In the area of face recognition, the last few years witnessed a dramatic reduction in error rates brought by new algorithms that are based on convolutional neural networks (CNN). Our company started using CNNs for the task of face recognition in 2013. The first application of neural networks alone resulted in an improvement in the accuracy of unconstrained face recognition by a factor of 15 times! We expect further improvements in face recognition performance due to explosive development of architectures and techniques related to CNNs.

As deep learning techniques kept proliferating other areas, we were ultimately able to employ our face recognition algorithms under the conditions of real-time surveillance. By being able to recognize and track other objects - such as pedestrians and all kinds of vehicles (cyclists, bikes, cars, busses, trucks, etc.) - in adjacent video frames, we can extract various pieces of information about those objects, for example the color of the vehicle or the direction of its movement. There is a separate modality for recognizing license plates of vehicles using neural networks.

Our latest improvements in other biometric modalities are also driven by extensive research in deep neural networks. This includes the most interoperable fingerprint algorithm in the world (ranked the first in the NIST MINEX interoperability category), the second most accurate iris recognition technology and the new version of our algorithm for speaker identification.

As another illustration of our achievements in using deep learning, Neurotechnology's researchers won first place in the 2017 Kaggle competition with a computer vision solution for classifying fish species.

We seek to distill our knowledge and understanding of how natural intelligence operates into deep-learning-based algorithms, and we see this approach as the shortest path toward achieving General AI. Neurotechnology also runs Deep Learning Paper Reviews, a series of open-access events devoted to sharing and discussing recent ideas in the field.

We have a reputation for developing a range of products for biometric identification of fingerprints, palm prints, faces, irises and voices. Since the release of our first fingerprint identification system, we have delivered more than 200 products and version upgrades for identification and verification of objects and personal identity. Over 3,000 system integrators, security companies and hardware providers in more than 140 countries integrate our algorithms into their products.

With a combination of fast algorithms and high reliability, our company's fingerprint, face, iris and voicebiometric technologies can be used for access control, computer security, banking, time attendance control and law enforcement applications, among others.

With millions of customer installations worldwide, our products are used for both civil and forensic applications, including border crossings, criminal investigations, systems for voter registration, verification and duplication checking, passport issuance and other national-scale projects.

Neurotechnology's fingerprint identification algorithms have shown some of the best results for reliability in major biometric competitions and evaluations, including the National Institute of Standards & Technology (NIST) Minutiae Interoperability Exchange III (MINEX III), Proprietary Fingerprint Template Evaluation II (PFT II) and Fingerprint Vendor Technology Evaluation for the US Department of Justice (FpVTE 2012). Previously, the fingerprint recognition algorithms have received awards in the International Fingerprint Verification Competitions (FVC2006, FVC2004, FVC2002 and FVC2000).

In 2018, our iris recognition algorithm was tested in the NIST Iris Exchange (IREX) Evaluation and was recognized as the second most accurate among those tested. The accelerated version of the algorithm was nearly 50 times faster than any other matching system in the NIST IREX IX evaluation. Previously, Neurotechnology showed outstanding results in the IREX, IREX III and IREX IV evaluations.

To meet the demands of a variety of applications, we developed many advanced algorithms based on computer vision. For instance, they are used in SentiSight.ai, our interactive web platform for developing AI-based object recognition applications. The platform has tools designed to support interactive model training - without coding - and faster image labeling, thus reducing the amount of user effort spent on data mining. We also run projects tailored to the specific needs of our customers.

Our technology for real-time surveillance was designed to support biometric face identification of moving pedestrians using live video streams from high-resolution digital cameras. The technology is used for passive identification, when passers-by do not make any efforts to be recognized. Possible uses for this technology include law enforcement, security, attendance control, visitor counting, traffic monitoring and other commercial applications.

Some of our ealier R&D efforts related to computer vision also involved technologies for eye movement tracking and 3D object model reconstruction.

Our current R&D effort in robotics is focused on the "programming by demonstration" approach and its practical implementation in various neural network structures. During our experiments we not only gained experience with different types of neural networks (e.g., various CNN's, RNN's, including RNN's with external memory, the self modifying RNN and the CMP, Cognitive Mapping and Planning), we also developed our own theoretical innovations.

SentiBotics, our ready-to-use robotics development kit, allows rapid development and testing of mobile robots. The kit includes a mobile robotic platform with a 3D vision system, a modular robotic arm and accompanying ROS-based software we have built on our own, with complete source code and programming samples.

Our Ultrasound Research Group undertakes research in the fields of ultrasonic particle manipulation, parametric array and transducer technology. It develops novel algorithms, hardware and electronics solutions for ultrasonics applications. Currently, the group is developing a new, patent-pending 3D printing and assembling technology based on ultrasonic particle manipulation. This new technology is intended to expand capabilities of existing 3D printing and assembling processes. In 2018, the group also filed a patent for a novel ultrasonic electrostatic transducer technology. These transducers were developed primarily for use in parametric array systems.