> For the complete documentation index, see [llms.txt](https://doc.ozforensics.com/oz-knowledge/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://doc.ozforensics.com/oz-knowledge/general/oz-liveness-and-biometry-key-concepts/liveness-face-matching-black-list-checks.md). # Liveness, Face Matching, Collection Checks This article describes the main types of analyses Oz software performs. * **Liveness** checks whether a person in a media is a real human. * **Face Matching** examines two or more media to identify similarities between the faces depicted in them. * **Collection** looks for resemblances between an individual featured in a media and individuals in a pre-existing photo database. These analyses are accessible in the Oz API for both SaaS and On-Premise models. Please visit [this page](/oz-knowledge/general/oz-liveness-and-biometry-key-concepts/saas-on-premise-on-device-what-to-choose.md) to learn more about the usage models. ### Liveness Liveness protects against two types of attacks: > A presentation attack, also known as a spoofing attack, refers to the attempt of an individual to deceive a facial recognition system by presenting into a camera video, photo, or any other type of media that mimics the appearance of a genuine user. These attacks can include the use of realistic masks or make up. > An injection attack is an attempt to deceive a facial recognition system by replacing physical camera input with a prerecorded image or video, manipulating physical camera output before it becomes input to a facial recognition, or injecting some malicious code. Virtual camera software is the most common tool for injection attacks. Oz Liveness is able to detect both types of attacks. Any component can detect presentation attacks, and for injection attack detection, use Oz Liveness SDK. To learn about how to use Oz components to prevent attacks, check our integration quick start guides: [How to Integrate Oz Liveness Using the Non-Persistent Oz API Mode](/oz-knowledge/general/integration-quick-start-guides/how-to-integrate-oz-liveness-using-the-non-persistent-oz-api-mode.md) [How to Integrate Oz Liveness into Your Web Application](/oz-knowledge/general/integration-quick-start-guides/how-to-integrate-oz-liveness-into-your-web-application.md) [How to Integrate Oz Liveness into Your Mobile Application](/oz-knowledge/general/integration-quick-start-guides/how-to-integrate-oz-liveness-into-your-mobile-application.md) [How to Perform a Face Matching Analysis](/oz-knowledge/general/integration-quick-start-guides/how-to-perform-a-face-matching-analysis.md) Once the Liveness check is finished, you can check the results.
Results overview * SUCCESS – everything went fine, the analysis has completed successfully; * DECLINED – the check failed (an attack detected). If the analysis hasn't been finished yet, the result can be PROCESSING (the analysis is in progress) / FAILED (the analysis failed due to some error and couldn't get finished). If you have analyzed multiple media, the aggregated status will be SUCCESS only if each analysis on each media has finished with the SUCCESS result.
Asking users to perform a gesture, such as smiling or turning their head, is a popular requirement when recording a Liveness video. With Oz Liveness Mobile and Web SDK, you can also request gestures from users. However, our Liveness check relies on other factors, analyzed by neural networks, and does not depend on gestures. For more details, please check [Passive and Active Liveness](/oz-knowledge/general/oz-liveness-and-biometry-key-concepts/passive-and-active-liveness.md). Liveness check also can return the best shot from a video: a best-quality frame where the face is seen the most properly. ### Face Matching The Biometry algorithm allows comparing several media and checking whether the people on them are the same person. As sources, you can use images, videos, and scans of documents (with photo). To perform the analysis, the algorithm requires at least two media.
Results overview * SUCCESS – everything went fine, the analysis has completed successfully; * DECLINED – the check failed (an attack detected). If the analysis hasn't been finished yet, the result can be PROCESSING (the analysis is in progress) / FAILED (the analysis failed due to some error and couldn't get finished). If you have analyzed multiple media, the aggregated status will be SUCCESS only if each analysis on each media has finished with the SUCCESS result.
To learn how to integrate Face Matching, please refer to the [integration quick start guides](/oz-knowledge/general/integration-quick-start-guides/face-matching.md). ### Collection In Oz API, you can configure one or more face collections. These collections are databases of people depicted in photos. When the Collection analysis is being conducted, Oz software compares the face in a photo or video taken with faces of this pre-made database and shows whether a face exists in a collection.
Results overview * SUCCESS – everything went fine, the analysis has completed successfully; * DECLINED – the check failed (an attack detected). If the analysis hasn't been finished yet, the result can be PROCESSING (the analysis is in progress) / FAILED (the analysis failed due to some error and couldn't get finished). If you have analyzed multiple media, the aggregated status will be SUCCESS only if each analysis on each media has finished with the SUCCESS result.
For additional information, please refer to [this article](/oz-knowledge/guides/developer-guide/api/oz-api/types-of-analyses-and-what-they-check.md). --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://doc.ozforensics.com/oz-knowledge/general/oz-liveness-and-biometry-key-concepts/liveness-face-matching-black-list-checks.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.