Explaining Face Presentation Attack Detection Using Natural Language
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Abstract (arXiv)
A large number of deep neural network based techniques have been developed to address the challenging problem of face presentation attack detection (PAD). Whereas such techniques' focus has been on improving PAD performance in terms of classification accuracy and robustness against unseen attacks and environmental conditions, there exists little attention on the explainability of PAD predictions. In this paper, we tackle the problem of explaining PAD predictions through natural language. Our approach passes feature representations of a deep layer of the PAD model to a language model to generate text describing the reasoning behind the PAD prediction. Due to the limited amount of annotated data in our study, we apply a light-weight LSTM network as our natural language generation model. We investigate how the quality of the generated explanations is affected by different loss functions, including the commonly used word-wise cross entropy loss, a sentence discriminative loss, and a sentence semantic loss. We perform our experiments using face images from a dataset consisting of 1,105 bona-fide and 924 presentation attack samples. Our quantitative and qualitative results show the effectiveness of our model for generating proper PAD explanations through text as well as the power of the sentence-wise losses. To the best of our knowledge, this is the first introduction of a joint biometrics-NLP task. Our dataset can be obtained through our GitHub page.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 33 of its 56 references that are in the map. First posted on arXiv 8 Nov 2021.
Builds on
33 of 56 references.
References · 33
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
- 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2017Squeeze-and-Excitation Networkscited by 4,789
- 2015Learning Deep Features for Discriminative Localizationcited by 2,436
- 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
- 2013Visualizing and Understanding Convolutional Networkscited by 2,290
- 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,423
- 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,264
21 more
- 2017Interpretable Explanations of Black Boxes by Meaningful Perturbationcited by 612
- 2016SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioningcited by 219
- 2020Searching Central Difference Convolutional Networks for Face Anti-Spoofingcited by 173
- 2018Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervisioncited by 167
- 2016Grad-CAM: Why did you say that?cited by 161
- 2016Generating Visual Explanationscited by 158
- 2016Learning a Deep Embedding Model for Zero-Shot Learningcited by 153
- 2015face anti-spoofing based on color texture analysiscited by 82
- 2014Learn Convolutional Neural Network for Face Anti-Spoofingcited by 80
- 2020Explainable Artificial Intelligence: a Systematic Reviewcited by 79
- 2020Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofingcited by 70
- 2018Face De-Spoofing: Anti-Spoofing via Noise Modelingcited by 60
- 2017Learning Robust Visual-Semantic Embeddingscited by 56
- 2019Learning Meta Model for Zero- and Few-shot Face Anti-spoofingcited by 44
- 2020On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofingcited by 44
- 2020Explainable Face Recognitioncited by 34
- 2021Detection and Continual Learning of Novel Face Presentation Attackscited by 33
- 2020Image Captioning through Image Transformercited by 29
- 2019Deep Anomaly Detection for Generalized Face Anti-Spoofingcited by 17
- 2020Multispectral Biometrics System Framework: Application to Presentation Attack Detectioncited by 6
- 2019RoPAD: Robust Presentation Attack Detection through Unsupervised Adversarial Invariancecited by 3
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