Proceedings ArticleDOI
An original face anti-spoofing approach using partial convolutional neural network
Lei Li,Xiaoyi Feng,Zinelabidine Boulkenafet,Zhaoqiang Xia,Li Mingming,Abdenour Hadid +5 more
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TLDR
This work extracts the deep partial features from the convolutional neural network (CNN) to distinguish the real and fake faces and uses the block principle component analysis (PCA) method to reduce the dimensionality of features that can avoid the over-fitting problem.Abstract:
Recently deep Convolutional Neural Networks have been successfully applied in many computer vision tasks and achieved promising results. So some works have introduced the deep learning into face anti-spoofing. However, most approaches just use the final fully-connected layer to distinguish the real and fake faces. Inspired by the idea of each convolutional kernel can be regarded as a part filter, we extract the deep partial features from the convolutional neural network (CNN) to distinguish the real and fake faces. In our prosed approach, the CNN is fine-tuned firstly on the face spoofing datasets. Then, the block principle component analysis (PCA) method is utilized to reduce the dimensionality of features that can avoid the over-fitting problem. Lastly, the support vector machine (SVM) is employed to distinguish the real the real and fake faces. The experiments evaluated on two public available databases, Replay-Attack and CASIA, show the proposed method can obtain satisfactory results compared to the state-of-the-art methods.read more
Citations
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Proceedings ArticleDOI
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision
TL;DR: This paper argues the importance of auxiliary supervision to guide the learning toward discriminative and generalizable cues, and introduces a new face anti-spoofing database that covers a large range of illumination, subject, and pose variations.
Journal ArticleDOI
Deep face recognition: A survey
Mei Wang,Weihong Deng +1 more
TL;DR: A comprehensive review of the recent developments on deep face recognition can be found in this paper, covering broad topics on algorithm designs, databases, protocols, and application scenes, as well as the technical challenges and several promising directions.
Proceedings ArticleDOI
Face anti-spoofing using patch and depth-based CNNs
TL;DR: A novel two-stream CNN-based approach for face anti-spoofing is proposed, by extracting the local features and holistic depth maps from the face images, which facilitate CNN to discriminate the spoof patches independent of the spatial face areas.
Proceedings ArticleDOI
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
TL;DR: Yu et al. as discussed by the authors proposed a frame level FAS method based on Central Difference Convolution (CDC), which is able to capture intrinsic detailed patterns via aggregating both intensity and gradient information.
Proceedings ArticleDOI
Deep Tree Learning for Zero-Shot Face Anti-Spoofing
TL;DR: Zhang et al. as mentioned in this paper proposed a novel Deep Tree Network (DTN) to tackle the zero-shot face anti-spoofing (ZSFA) problem by partitioning the spoof samples into semantic sub-groups in an unsupervised fashion.
References
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