Learning Meta Pattern for Face Anti-Spoofing
نویسندگان
چکیده
Face Anti-Spoofing (FAS) is essential to secure face recognition systems and has been extensively studied in recent years. Although deep neural networks (DNNs) for the FAS task have achieved promising results intra-dataset experiments with similar distributions of training testing data, DNNs' generalization ability limited under cross-domain scenarios different data. To improve ability, hybrid methods explored extract task-aware handcrafted features (e.g., Local Binary Pattern) as discriminative information input DNNs. However, feature extraction relies on experts' domain knowledge, how choose appropriate underexplored. this end, we propose a learnable network Meta Pattern (MP) our learning-to-learn framework. By replacing MP, from MP capable learning more generalized model. Moreover, devise two-stream hierarchically fuse RGB image extracted by using proposed Hierarchical Fusion Module (HFM). We conduct comprehensive show that outperforms compared features. Also, method HFM can achieve state-of-the-art performance two evaluation benchmarks.
منابع مشابه
Learn Convolutional Neural Network for Face Anti-Spoofing
Though having achieved some progresses, the hand-crafted texture features, e.g., LBP [23], LBP-TOP [11] are still unable to capture the most discriminative cues between genuine and fake faces. In this paper, instead of designing feature by ourselves, we rely on the deep convolutional neural network (CNN) to learn features of high discriminative ability in a supervised manner. Combined with some...
متن کاملLearning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision
Face anti-spoofing is crucial to prevent face recognition systems from a security breach. Previous deep learning approaches formulate face anti-spoofing as a binary classification problem. Many of them struggle to grasp adequate spoofing cues and generalize poorly. In this paper, we argue the importance of auxiliary supervision to guide the learning toward discriminative and generalizable cues....
متن کاملFace-Spoofing 2D-Detection Based on Moiré-Pattern Analysis
Biometric systems based on face recognition have been shown unreliable under the presence of face-spoofing images. Hence, automatic solutions for spoofing detection became necessary. In this paper, face-spoofing detection is proposed by searching for Moiré patterns due to the overlap of the digital grids. The conditions under which these patterns arise are first described, and their detection i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Information Forensics and Security
سال: 2022
ISSN: ['1556-6013', '1556-6021']
DOI: https://doi.org/10.1109/tifs.2022.3158551