Face liveliness detection is a technology that is used to determine if the person presenting the face is actually present and alive, and not a still photo or a video. It is often used as a security measure that require face recognition, such as banking or financial apps. Face liveliness detection using ML involves training machine learning models to recognize and differentiate between real and fake faces. The ML algorithms are trained on large datasets of faces to learn how to distinguish between a live face and a photo, video or other fake representation of a face.

The ML model detects patterns and features that are unique to live faces, such as facial movements, eye blinks etc. These features are detected by the model in real-time by analysing video frames or still images captured by the camera on a mobile device.

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To develop an ML model for face liveliness detection, the following steps are taken:

- Collect and pre-process the data

- Train the ML model

- Test and validate the ML model

- Integrate the ML model into the mobile app

 

ML-based face liveliness detection provides a more accurate and reliable way to detect fake faces compared to traditional methods. It is also more difficult for fraudsters to deceive ML-based detection mechanisms as the algorithms can analyse multiple features simultaneously and recognize subtle changes in facial movements. This can improve the security and trustworthiness of mobile apps that require face recognition for authentication purposes.