Oz Liveness and Biometry Key Concepts
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Oz Forensics specializes in liveness and face matching: we develop products that help you to identify your clients remotely and avoid any kind of spoofing or deepfake attack. Oz software helps you to add facial recognition to your software systems and products. You can integrate Oz modules in many ways depending on your needs. We are constantly improving our components, increasing their quality.
Oz Liveness is responsible for recognizing a living person on a video it receives. Oz Liveness can distinguish a real human from their photo, video, mask, or other kinds of spoofing and deepfake attacks. The algorithm is certified in ISO-30137-3 standard by NIST accreditation iBeta biometric test laboratory with 100% accuracy.
Our liveness technology protects both against injection and presentation attacks.
The injection attack detection is layered. Our SDK examines user environment to detect potential manipulations: browser, camera, etc. Further on, the deep neural network comes into play to defend against even the most sophisticated injection attacks.
Oz Face Matching (Biometry) aims to identify the person, verifying that the person who performs the check and the papers' owner are the same person. Oz Biometry looks through the video, finds the best shot where the person is clearly seen, and compares it with the photo from ID or another document. The algorithm's accuracy is 99.99% confirmed by NIST FRVT.
Our biometry technology has both 1:1 Face Verification and 1:N Face Identification, which are also based on ML algorithms. To train our neural networks, we use an own framework based on state-of-the-art technologies. The large private dataset (over 4.5 million unique faces) with a wide representation of ethnic groups as well as using other attributes (predicted race, age, etc.) helps our biometric models to provide the robust matching scores.
Our face detector can work with photos and videos. Also, the face detector excels in detecting faces in images of IDs and passports (which can be rotated or of low quality).
The presentation attack detection is based on deep neural networks of various architectures, combined with a proprietary ensembling algorithm to achieve optimal performance. The networks consider multiple factors, including reflection, focus, background scene, motion patterns, etc. We offer both passive (no gestures) and active (various gestures) , ensuring that your customers enjoy the user experience while delivering accurate results for you. The iBeta test was conducted using passive Liveness, and since then, we have significantly enhanced our networks to better meet the needs of our clients.
The Oz software combines accuracy in analysis with ease of integration and use. To further simplify the integration process, we have provided a detailed description of all the key concepts of our system in this section. If you're ready to get started, please refer to our , which provide the step-by-step instructions on how to achieve your facial recognition goals quickly and easily.
