Wu et al., 2020 - Google Patents
Single-shot face anti-spoofing for dual pixel cameraWu et al., 2020
- Document ID
- 9125738266929269118
- Author
- Wu X
- Zhou J
- Liu J
- Ni F
- Fan H
- Publication year
- Publication venue
- IEEE Transactions on Information Forensics and Security
External Links
Snippet
In this study, we propose a neural network-based face anti-spoofing algorithm using dual pixel (DP) sensor images. The proposed algorithm has two stages: depth reconstruction and depth classification. The first network takes a DP image pair as input and generates a depth …
- 230000001537 neural 0 abstract description 20
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00288—Classification, e.g. identification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00597—Acquiring or recognising eyes, e.g. iris verification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Truong et al. | Pdc-net+: Enhanced probabilistic dense correspondence network | |
Wang et al. | 360sd-net: 360 stereo depth estimation with learnable cost volume | |
Zhou et al. | Two-stream neural networks for tampered face detection | |
Yu et al. | Learning dense facial correspondences in unconstrained images | |
CN109190581B (en) | Image sequence target detection and identification method | |
CN103325112B (en) | Moving target method for quick in dynamic scene | |
Wu et al. | Single-shot face anti-spoofing for dual pixel camera | |
CN112052831B (en) | Method, device and computer storage medium for face detection | |
Guo et al. | Improving face anti-spoofing by 3D virtual synthesis | |
Joung et al. | Unsupervised stereo matching using confidential correspondence consistency | |
Wan et al. | Drone image stitching using local mesh-based bundle adjustment and shape-preserving transform | |
CN112329662B (en) | Multi-view saliency estimation method based on unsupervised learning | |
Liu et al. | Physics-guided spoof trace disentanglement for generic face anti-spoofing | |
Yan et al. | Deep learning on image stitching with multi-viewpoint images: A survey | |
Wang et al. | Paul: Procrustean autoencoder for unsupervised lifting | |
Song et al. | Multistage curvature-guided network for progressive single image reflection removal | |
Kang et al. | Facial depth and normal estimation using single dual-pixel camera | |
Yuan et al. | Structure flow-guided network for real depth super-resolution | |
Manda et al. | Image stitching using ransac and bayesian refinement | |
Li et al. | Monocular human depth estimation with 3D motion flow and surface normals | |
Chu et al. | Semi-supervised 3d human pose estimation by jointly considering temporal and multiview information | |
Khan et al. | Towards monocular neural facial depth estimation: Past, present, and future | |
Yang et al. | Deep convolutional grid warping network for joint depth map upsampling | |
Ma et al. | Learning Spatial–Parallax Prior Based on Array Thermal Camera for Infrared Image Enhancement | |
CN112380966B (en) | Monocular iris matching method based on feature point re-projection |