Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation

• URL: http://arxiv.org/abs/2304.13409v2
• Date: Tue, 7 Nov 2023 11:54:17 GMT
• Title: Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation
• Authors: Marco Huber, Anh Thi Luu, Philipp Terh\"orst, Naser Damer
• Abstract summary: Understanding why two faces images are matched or not matched by a given face recognition system is important. We propose xSSAB, an approach to back-propagate similarity score-based arguments that support or oppose the face matching decision. We present Patch-LFW, a new explainable face verification benchmark that enables along with a novel evaluation protocol.
• Score: 5.956239490189115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Explainable Face Recognition is gaining growing attention as the use of the technology is gaining ground in security-critical applications. Understanding why two faces images are matched or not matched by a given face recognition system is important to operators, users, anddevelopers to increase trust, accountability, develop better systems, and highlight unfair behavior. In this work, we propose xSSAB, an approach to back-propagate similarity score-based arguments that support or oppose the face matching decision to visualize spatial maps that indicate similar and dissimilar areas as interpreted by the underlying FR model. Furthermore, we present Patch-LFW, a new explainable face verification benchmark that enables along with a novel evaluation protocol, the first quantitative evaluation of the validity of similarity and dissimilarity maps in explainable face recognition approaches. We compare our efficient approach to state-of-the-art approaches demonstrating a superior trade-off between efficiency and performance. The code as well as the proposed Patch-LFW is publicly available at: https://github.com/marcohuber/xSSAB.

Related papers

• Towards A Comprehensive Visual Saliency Explanation Framework for AI-based Face Recognition Systems [9.105950041800225]
  This manuscript conceives a comprehensive explanation framework for face recognition tasks. An exhaustive definition of visual saliency map-based explanations for AI-based face recognition systems is provided. A new model-agnostic explanation method named CorrRISE is proposed to produce saliency maps.
  arXiv  Detail & Related papers  (2024-07-08T14:25:46Z)
• Improving Face Recognition from Caption Supervision with Multi-Granular Contextual Feature Aggregation [0.0]
  We introduce caption-guided face recognition (CGFR) as a new framework to improve the performance of commercial-off-the-shelf (COTS) face recognition systems. We implement the proposed CGFR framework on two face recognition models (ArcFace and AdaFace) and evaluated its performance on the Multi-Modal CelebA-HQ dataset.
  arXiv  Detail & Related papers  (2023-08-13T23:52:15Z)
• Explanation of Face Recognition via Saliency Maps [13.334500258498798]
  This paper proposes a rigorous definition of explainable face recognition (XFR) It then introduces a similarity-based RISE algorithm (S-RISE) to produce high-quality visual saliency maps. An evaluation approach is proposed to systematically validate the reliability and accuracy of general visual saliency-based XFR methods.
  arXiv  Detail & Related papers  (2023-04-12T19:04:21Z)
• DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain [23.4606547767188]
  DuetFace is a privacy-preserving face recognition method that employs collaborative inference in the frequency domain. The proposed method achieves a comparable recognition accuracy and cost to the unprotected ArcFace and outperforms the state-of-the-art privacy-preserving methods.
  arXiv  Detail & Related papers  (2022-07-15T08:35:44Z)
• TransFA: Transformer-based Representation for Face Attribute Evaluation [87.09529826340304]
  We propose a novel textbftransformer-based representation for textbfattribute evaluation method (textbfTransFA) The proposed TransFA achieves superior performances compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-07-12T10:58:06Z)
• QMagFace: Simple and Accurate Quality-Aware Face Recognition [5.5284501467256515]
  We propose a simple and effective face recognition solution (QMag-Face) that combines a quality-aware comparison score with a recognition model based on a magnitude-aware angular margin loss. The experiments conducted on several face recognition databases and benchmarks demonstrate that the introduced quality-awareness leads to consistent improvements in the recognition performance.
  arXiv  Detail & Related papers  (2021-11-26T12:44:54Z)
• Asymmetric Modality Translation For Face Presentation Attack Detection [55.09300842243827]
  Face presentation attack detection (PAD) is an essential measure to protect face recognition systems from being spoofed by malicious users. We propose a novel framework based on asymmetric modality translation forPAD in bi-modality scenarios. Our method achieves state-of-the-art performance under different evaluation protocols.
  arXiv  Detail & Related papers  (2021-10-18T08:59:09Z)
• Learning Fair Face Representation With Progressive Cross Transformer [79.73754444296213]
  We propose a progressive cross transformer (PCT) method for fair face recognition. We show that PCT is capable of mitigating bias in face recognition while achieving state-of-the-art FR performance.
  arXiv  Detail & Related papers  (2021-08-11T01:31:14Z)
• Inter-class Discrepancy Alignment for Face Recognition [55.578063356210144]
  We propose a unified framework calledInter-class DiscrepancyAlignment(IDA) IDA-DAO is used to align the similarity scores considering the discrepancy between the images and its neighbors. IDA-SSE can provide convincing inter-class neighbors by introducing virtual candidate images generated with GAN.
  arXiv  Detail & Related papers  (2021-03-02T08:20:08Z)
• Face Anti-Spoofing Via Disentangled Representation Learning [90.90512800361742]
  Face anti-spoofing is crucial to security of face recognition systems. We propose a novel perspective of face anti-spoofing that disentangles the liveness features and content features from images.
  arXiv  Detail & Related papers  (2020-08-19T03:54:23Z)
• Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition [90.26023388850771]
  This paper presents a deep neural network approach to extract decorrelation representations in a hyperspherical space for cross-domain face images. The proposed framework can be divided into two components: heterogeneous representation network and decorrelation representation learning. Experimental results on two challenging heterogeneous face databases show that our approach achieves superior performance on both verification and recognition tasks.
  arXiv  Detail & Related papers  (2020-05-25T07:01:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.