Adversarial Masking Contrastive Learning for vein recognition

no code implementations • 16 Jan 2024 • Huafeng Qin, Yiquan Wu, Mounim A. El-Yacoubi, Jun Wang, Guangxiang Yang

To overcome this problem, in this paper, we propose an adversarial masking contrastive learning (AMCL) approach, that generates challenging samples to train a more robust contrastive learning model for the downstream palm-vein recognition task, by alternatively optimizing the encoder in the contrastive learning model and a set of latent variables.

Contrastive Learning Generative Adversarial Network

EmMixformer: Mix transformer for eye movement recognition

no code implementations • 10 Jan 2024 • Huafeng Qin, Hongyu Zhu, Xin Jin, Qun Song, Mounim A. El-Yacoubi, Xinbo Gao

To this end, we propose a mixed block consisting of three modules, transformer, attention Long short-term memory (attention LSTM), and Fourier transformer.

Adversarial AutoMixup

2 code implementations • 19 Dec 2023 • Huafeng Qin, Xin Jin, Yun Jiang, Mounim A. El-Yacoubi, Xinbo Gao

In this paper, we propose AdAutomixup, an adversarial automatic mixup augmentation approach that generates challenging samples to train a robust classifier for image classification, by alternatively optimizing the classifier and the mixup sample generator.

Classification Image Classification

Multi-Scale and Multi-Direction GAN for CNN-Based Single Palm-V ein Identification

no code implementations • IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2021 • Huafeng Qin, Mounim A. El-Y acoubi, Y a n t a o L i, Member, IEEE, and Chongwen Liu

Despite recent advances of deep neural networks in hand vein identification, the existing solutions assume the availability of a large and rich set of training image samples.

Data Augmentation Generative Adversarial Network