no code implementations • 1 Jul 2021 • Konstantin Bulatov, Ekaterina Emelianova, Daniil Tropin, Natalya Skoryukina, Yulia Chernyshova, Alexander Sheshkus, Sergey Usilin, Zuheng Ming, Jean-Christophe Burie, Muhammad Muzzamil Luqman, Vladimir V. Arlazarov
Identity documents recognition is an important sub-field of document analysis, which deals with tasks of robust document detection, type identification, text fields recognition, as well as identity fraud prevention and document authenticity validation given photos, scans, or video frames of an identity document capture.
The widespread deployment of face recognition-based biometric systems has made face Presentation Attack Detection (face anti-spoofing) an increasingly critical issue.
Face recognition of realistic visual images has been well studied and made a significant progress in the recent decade.
Rather than the visual images, the face recognition of the caricatures is far from the performance of the visual images.
This multi-task learning with dynamic weights also boosts of the performance on the different tasks comparing to the state-of-art methods with single-task learning.
Ranked #1 on Facial Expression Recognition (FER) on Oulu-CASIA
Benefiting from the advance of deep convolutional neural network approaches (CNNs), many face detection algorithms have achieved state-of-the-art performance in terms of accuracy and very high speed in unconstrained applications.
This paper proposes a holistic multi-task Convolutional Neural Networks (CNNs) with the dynamic weights of the tasks, namely FaceLiveNet+, for face authentication.