Liveness Detection is an ideal biometric modality for mobile authentication. It is intuitive and adaptable to most mobile devices, with widespread camera integration in commercial devices. It works with a familiar “selfie” pose. However, the widespread availability of digital facial images via social media makes facial biometrics more susceptible to spoofing. For this reason, it is critical to apply robust liveness detection for mobile biometric authentication solutions that use facial recognition.
In facial recognition, liveness detection role is used distinguish between a live image and a 2D printed, 3D printed, or digital representation of a user’s face. Spoof attempts can be detected through algorithms that recognize artifacts of a non-live sample. Liveness detection methods significantly reduce the effectiveness of spoofing and other presentation attacks.
ML Kit is a mobile SDK that brings Google's machine learning expertise to Android and iOS apps in a powerful yet easy-to-use package. Whether you're new or experienced in machine learning, you can implement the functionality you need in just a few lines of code. There's no need to have deep knowledge of neural networks or model optimization to get started. On the other hand, if you are an experienced ML developer, ML Kit provides convenient APIs that help you use your custom TensorFlow Lite models in your mobile apps.
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Please make sure to update tests as appropriate.