> For the complete documentation index, see [llms.txt](https://docs.vida.id/identity-stack/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.vida.id/identity-stack/verify/liveness/overview.md).

# Overview

VIDA offers multiple liveness detection modes to ensure robust and secure user verification across various use cases. Supported modes include:

1. [**Passive Liveness**](#passive-liveness)
2. [**Gesture Liveness**](#gesture-liveness)
3. [**Zoom** **Liveness**](#zoom-liveness)
4. [**Color Flash**](#color-flash)

Each mode is designed to counter different types of spoofing attempts and can be configured based on your application’s security requirements.

***

## **Passive Liveness** 

Passive Liveness operates in the background, analyzing selfies with advanced AI and ML techniques to confirm the image's authenticity without requiring user interaction. It generates a selfie score from 0 to 1 , basis on which the genuineness of the selfie is determined. By default, passive liveness is enabled.

## **Gesture Liveness**

Gesture Liveness involves user interaction, where the system prompts the user to perform simple actions like blinking, smiling, or moving their head. This method ensures that the user is live and present, making it harder for attackers to spoof the system. 

Requires a simple user action (such as a head movement). This method includes checks for **injection attacks**, **image quality**, **liveness**, and **image manipulation**.

## **Zoom Liveness**

Zoom Liveness requires a series of gestures including **eyeblink, smile, head shake, zoom gesture, and face frame validation.** These actions provide stronger security by enabling deeper analysis of motion, depth, and facial cues.

## **Color Flash**

Color Flash-based liveness works by flashing specific colors on the screen and analyzing the reflection of those colors on the user's face in real time. This ensures that the user is physically present and not a static image or replay attack, enhancing the robustness of the liveness check through light-based interaction.

### Comparison between All Liveness

<table><thead><tr><th width="129.597412109375"></th><th width="148.82373046875">Passive liveness</th><th>Gesture Liveness</th><th>Zoom Liveness</th><th>Color Flash</th></tr></thead><tbody><tr><td><strong>Checks carried out</strong></td><td><ul><li>Image Quality check</li><li>Liveness check and Image Manipulation Check</li></ul></td><td><p>All in passive liveness, and:</p><ul><li>Eye Blink Check</li><li>Smile Check</li><li>Head Movement Check</li></ul></td><td><p>All in passive liveness, and:</p><ul><li>Face Zoom Check</li><li>Eye Blink Check</li><li>Smile Check</li><li>Head Movement Check</li></ul></td><td><p></p><p>All in passive liveness, and:</p><ul><li>Reflection Check</li><li>Color Check</li><li>Liveness HD</li></ul></td></tr><tr><td><strong>Benefit</strong></td><td><ul><li>Easy to implement and convenient for users</li><li>The photo taking process is faster</li></ul></td><td><ul><li>Provides higher security because it requires active user interactions (physical movements or actions).</li><li>Hard to imitate using masks, printed photos, screen replays, or AI-generated selfies.</li><li>Difficult to spoof with pre-recorded videos since the prompts are random and unpredictable.</li></ul></td><td><ul><li>Adds motion-based depth analysis for spoof resistance</li><li>Simple and intuitive for users to follow</li><li>Effective against printed photos and screen replays</li></ul></td><td><ul><li>Simplifies liveness verification by removing the need for active gestures (blinking, smiling, nodding).</li><li>Users only need to look at the screen while a sequence of colors flashes.</li><li>Enhances security by detecting depth, skin texture, and dynamic light response.</li></ul></td></tr></tbody></table>

### Image Manipulation Detection

To further enhance security, VIDA incorporates an Image Manipulation Detection check as an additional layer of scrutiny. Learn more about this process on our [**Image Manipulation Detection.**](/identity-stack/verify/liveness/overview/image-manipulation-detection.md)

### Liveness Threshold

VIDA also applies a **default** [**liveness threshold**](/identity-stack/verify/liveness/overview/liveness-threshold.md) **of 0.95**, which determines whether a selfie is accepted as live or rejected as spoofed based on the liveness score range of **0.0–1.0.**

### Image Quality Assessment

VIDA evaluates the quality of captured images to minimize false rejections due to poor image quality. The VIDA image quality standard adheres to ISO/IEC 29794-5.

<table><thead><tr><th width="267.8203125">Image Quality Check Component</th><th>API</th><th>SDK</th></tr></thead><tbody><tr><td>Under exposure</td><td>Error response feedback</td><td>Real time feedback</td></tr><tr><td>Over exposure</td><td>Error response feedback</td><td>Real time feedback</td></tr><tr><td>Blur</td><td>Error response feedback</td><td>Real time feedback</td></tr><tr><td>Eye visibility</td><td>Error response feedback</td><td>Real time feedback</td></tr><tr><td>Occlusion prevention</td><td>Error response feedback</td><td>Real time feedback</td></tr><tr><td>Eyes open</td><td>Error response feedback</td><td>Real time feedback</td></tr><tr><td>Crop of face image prevention</td><td>Error response feedback</td><td>Real time feedback</td></tr></tbody></table>