Pinned Repositories
Live-face-detection
Face liveness helps to differentiate the feature space into ‘live’ and ‘not-live’ objects, forging the need for the users to be physically present to access their accounts. The objective of this challenge is to detect if the face in front of the camera belongs to a live person or not. It should detect liveness against printed-photo attach, replay attack, and 3-D mask attack. The result should contain a confidence score of liveness [0-1] for the given image. You can ignore non-face images. Also, all the closed-eye faces are not-live. The minimum project submission accuracy is 90%.
ggresearch
ggresearch
ai-hub-models
The Qualcomm® AI Hub Models are a collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices.
DLExpert's Repositories
DLExpert/ggresearch
ggresearch