# face anti-spoofing based on color texture analysis

arXiv: https://arxiv.org/abs/1511.06316 · HTML: https://mlsanity.com/paper/1511.06316/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2015.

## Built on by (most cited first, 30 of 82)

- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [Thin-Plate Spline Motion Model for Image Animation](https://mlsanity.com/paper/2203.14367/) (2022)
- [Animate-X: Universal Character Image Animation with Enhanced Motion Representation](https://mlsanity.com/paper/2410.10306/) (2024)
- [Deep Learning for Face Anti-Spoofing: A Survey](https://mlsanity.com/paper/2106.14948/) (2021)
- [Animate Anyone 2: High-Fidelity Character Image Animation with Environment Affordance](https://mlsanity.com/paper/2502.06145/) (2025)
- [Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing](https://mlsanity.com/paper/2108.02667/) (2021)
- [Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment](https://mlsanity.com/paper/2303.13662/) (2023)
- [Dual-Cross Central Difference Network for Face Anti-Spoofing](https://mlsanity.com/paper/2105.01290/) (2021)
- [Dual Reweighting Domain Generalization for Face Presentation Attack Detection](https://mlsanity.com/paper/2106.16128/) (2021)
- [Instance-Aware Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2304.05640/) (2023)
- [Cross Modal Focal Loss for RGBD Face Anti-Spoofing](https://mlsanity.com/paper/2103.00948/) (2021)
- [TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection](https://mlsanity.com/paper/2104.07419/) (2021)
- [SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models](https://mlsanity.com/paper/2402.04178/) (2024)
- [Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing](https://mlsanity.com/paper/2203.12175/) (2022)
- [Meta-Teacher For Face Anti-Spoofing](https://mlsanity.com/paper/2111.06638/) (2021)
- [Generative Domain Adaptation for Face Anti-Spoofing](https://mlsanity.com/paper/2207.10015/) (2022)
- [Adaptive Mixture of Experts Learning for Generalizable Face Anti-Spoofing](https://mlsanity.com/paper/2207.09868/) (2022)
- [Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era](https://mlsanity.com/paper/2202.10673/) (2022)
- [Gradient Alignment for Cross-Domain Face Anti-Spoofing](https://mlsanity.com/paper/2402.18817/) (2024)
- [Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues](https://mlsanity.com/paper/2208.05401/) (2022)
- [Feature Generation and Hypothesis Verification for Reliable Face Anti-Spoofing](https://mlsanity.com/paper/2112.14894/) (2021)
- [FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing](https://mlsanity.com/paper/2305.03277/) (2023)
- [Flexible-Modal Face Anti-Spoofing: A Benchmark](https://mlsanity.com/paper/2202.08192/) (2022)
- [Real Masks and Spoof Faces: On the Masked Face Presentation Attack Detection](https://mlsanity.com/paper/2103.01546/) (2021)
- [Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning](https://mlsanity.com/paper/2112.00568/) (2021)
- [Test-Time Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2403.19334/) (2024)
- [Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing](https://mlsanity.com/paper/2302.05744/) (2023)
- [Structure Destruction and Content Combination for Face Anti-Spoofing](https://mlsanity.com/paper/2107.10628/) (2021)
- [Digital and Physical Face Attacks: Reviewing and One Step Further](https://mlsanity.com/paper/2209.14692/) (2022)
- [Learnable Differencing Center for Nighttime Depth Perception](https://mlsanity.com/paper/2306.14538/) (2023)

## Abstract (arXiv)

Research on face spoofing detection has mainly been focused on analyzing the luminance of the face images, hence discarding the chrominance information which can be useful for discriminating fake faces from genuine ones. In this work, we propose a new face anti-spoofing method based on color texture analysis. We analyze the joint color-texture information from the luminance and the chrominance channels using a color local binary pattern descriptor. More specifically, the feature histograms are extracted from each image band separately. Extensive experiments on two benchmark datasets, namely CASIA face anti-spoofing and Replay-Attack databases, showed excellent results compared to the state-of-the-art. Most importantly, our inter-database evaluation depicts that the proposed approach showed very promising generalization capabilities.