FaceLiveNet+: A Holistic Networks For Face Authentication Based On Dynamic Multi-task Convolutional Neural Networks

TL;DR

FaceLiveNet+ introduces a multi-task CNN with dynamic task weights for improved face authentication by simultaneously performing face verification and facial expression recognition, demonstrating superior performance over single-task models.

Contribution

The paper presents a novel multi-task CNN with a dynamic-weight-unit that automatically learns task weights, enhancing face authentication and liveness detection capabilities.

Findings

Multi-task learning outperforms single-task learning in face verification and expression recognition.

Dynamic weights improve training effectiveness of the multi-task network.

Proposed protocol demonstrates feasibility of FaceLiveNet+ for face authentication.

Abstract

This paper proposes a holistic multi-task Convolutional Neural Networks (CNNs) with the dynamic weights of the tasks,namely FaceLiveNet+, for face authentication. FaceLiveNet+ can employ face verification and facial expression recognition as a solution of liveness control simultaneously. Comparing to the single-task learning, the proposed multi-task learning can better capture the feature representation for all of the tasks. The experimental results show the superiority of the multi-task learning to the single-task learning for both the face verification task and facial expression recognition task. Rather using a conventional multi-task learning with fixed weights for the tasks, this work proposes a so called dynamic-weight-unit to automatically learn the weights of the tasks. The experiments have shown the effectiveness of the dynamic weights for training the networks. Finally, the holistic evaluation for face authentication based on the proposed protocol has shown the feasibility to apply the FaceLiveNet+ for face authentication.