Artificial Intelligence

White Paper: Jumio

Biometrics and AI Build the Strongest ID Verification Tech Biometrics are commonplace. They protect our phones, log us into our virtual workspaces, secure our health records and verify our identity when we sign up for new services. Face, fingerprint, iris, voice and other modalities proliferate across our physical and digital lives, facilitating access, managing identity and keeping us safe from fraud. But it is crucial to understand that not all biometric technology is created equal. The fact is, there are smart biometrics and basic ones. Functionally, allbtrue biometrics, regardless of the modality, do what the name implies: measure unique physical or behavioral traits and compare them. Some consumer-grade biometric solutions keep it simple, measuring and comparing and matching the same way every time. On the other hand, smart biometrics, which are enhanced by artificial intelligence and machine learning, adapt with every use, getting stronger, faster and more scalable. The latter type is a foundational aspect of a broader trend that FindBiometrics calls “Intelligent ID” – a key technology for the future of our increasingly digital and mobile lives. Artificial intelligence is a heavy term in our culture, and it brings with it a great deal of baggage in the form of common misconceptions. When it comes to intelligent biometric identity, these misconceptions pool around face biometrics, identity proofing and continuous authentication, which taken together are the basic components of a trust chain. Fears about user privacy, distrust stemming from racial bias reports in surveillance systems, and the expectation that identity proofing must rely on a human element in the onboarding process are all common false precepts clouding the understanding of smart biometrics in ID verification and user authentication.

White Paper: IDS

Deep learning opens up new fields of application for industrial image processing, which previously could only be solved with great effort or not at all. The new, fundamentally different approach to classical image processing causes new challenges for users – it’s necessary to think differently. Therefore, IDS presents an all-in-one embedded vision solution with which every user can implement AI-based image processing in just a few steps without programming knowledge, and execute a neural net directly on an industrial camera. This makes deep learning particularly user-friendly. Computer vision and image processing have become indispensable tools in various application fields. Image processing systems are increasingly confronted with a constantly growing variety of products and variants and organic objects such as fruit, vegetables or plants. Conventional approaches with rule-based image processing quickly reach their limits if the image data to be analyzed varies too frequently and the differences are difficult or impossible to describe using algorithms. In such cases, a robust automation is not feasible due to an inflexible set of rules, even if the task is supposed to be easy for humans to solve. As an example, a child is able to recognize a car even if it has never seen the special model before. It is sufficient if the child has seen enough other car models previously.

White Paper: Zemax

How Modern Virtual Prototyping Can Change The Product Development Process? Outdated workflows limit team collaboration and compromise design integrity causing failures in physical prototypes or production impacting the schedules of an organization. As a result, leading companies have moved away from a linear process to a parallel design environment, in which design faithfulness is maintained throughout the workflow. By collaborating over a shared virtual prototype, optical and mechanical engineers can easily identify and correct design errors early in the design phase. Having the product designed right in the early stage leads to higher top-line revenue, lower cost, and a faster time to market. By harnessing the power of OpticStudio and LensMechanix, optical and mechanical engineers can share complete design data and analyze optical performance in a virtual model, reducing design iterations and physical prototypes—saving time and money. Move ahead and read the following whitepaper that will address all your questions, including these: How outdated workflows limit team collaboration and cause design delays?​ What is the importance of modern virtual prototyping?​ How OpticStudio and LensMechanix together can create a modern virtual prototyping solution?