Quality based frame selection is a crucial task in video face recognition, to both improve the recognition rate and to reduce the computational cost. In this paper we present a framework that uses a ...

Generating a talking face video from the input audio sequence is a practical yet challenging task. Most existing methods either fail to capture fine facial details or need to train a specific model ...

After that, we carry out a series of subjective experiments to evaluate the quality of multi-attribute RF images from various perspectives, and construct the Multi-Attribute Retouched Face Database ...

The widespread use of super-resolution methods, in a variety of applications such as surveillance has led to an increasing need for or quality assessment measures. The current quality measures aim to ...

FaceQnet is a No-Reference, end-to-end Quality Assessment (QA) system for face recognition based on deep learning. The system consists of a Convolutional Neural Network that is able to predict the ...

Face Image Quality Assessment (FIQA) lays the foundation for ensuring the stability and accuracy of face recognition systems. However, existing FIQA methods mainly formulate quality relationships ...

Collaborative learning has gained significant traction for training deep learning models without sharing the original data of participants, particularly when dealing with sensitive data such as facial ...

Existing reference-based blind face restoration (BFR) methods tend to either focus on the detailed texture of facial components or only conduct code prediction in the latent space, often neglecting ...

The 3D face quality assessment framework is used for quality inspection of the 3D face reconstructions models. It mainly detects the holes of the 3D face model, counts the number of holes, and returns ...

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