From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processing

• URL: http://arxiv.org/abs/2409.16089v1
• Date: Tue, 24 Sep 2024 13:40:39 GMT
• Title: From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processing
• Authors: Ivan DeAndres-Tame, Muhammad Faisal, Ruben Tolosana, Rouqaiah Al-Refai, Ruben Vera-Rodriguez, Philipp Terhörst,
• Abstract summary: Face Recognition (FR) has advanced significantly with the development of deep learning, achieving high accuracy in several applications. The lack of interpretability of these systems raises concerns about their accountability, fairness, and reliability. We propose an interactive framework to enhance the explainability of FR models by combining model-agnostic Explainable Artificial Intelligence (XAI) and Natural Language Processing (NLP) techniques.
• Score: 2.7568948557193287
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Face Recognition (FR) has advanced significantly with the development of deep learning, achieving high accuracy in several applications. However, the lack of interpretability of these systems raises concerns about their accountability, fairness, and reliability. In the present study, we propose an interactive framework to enhance the explainability of FR models by combining model-agnostic Explainable Artificial Intelligence (XAI) and Natural Language Processing (NLP) techniques. The proposed framework is able to accurately answer various questions of the user through an interactive chatbot. In particular, the explanations generated by our proposed method are in the form of natural language text and visual representations, which for example can describe how different facial regions contribute to the similarity measure between two faces. This is achieved through the automatic analysis of the output's saliency heatmaps of the face images and a BERT question-answering model, providing users with an interface that facilitates a comprehensive understanding of the FR decisions. The proposed approach is interactive, allowing the users to ask questions to get more precise information based on the user's background knowledge. More importantly, in contrast to previous studies, our solution does not decrease the face recognition performance. We demonstrate the effectiveness of the method through different experiments, highlighting its potential to make FR systems more interpretable and user-friendly, especially in sensitive applications where decision-making transparency is crucial.

Related papers

• Interaction as Explanation: A User Interaction-based Method for Explaining Image Classification Models [1.3597551064547502]
  In computer vision, explainable AI (xAI) methods seek to mitigate the 'black-box' problem. Traditional xAI methods concentrate on visualizing input features that influence model predictions. We present an interaction-based xAI method that enhances user comprehension of image classification models through their interaction.
  arXiv  Detail & Related papers  (2024-04-15T14:26:00Z)
• Diffexplainer: Towards Cross-modal Global Explanations with Diffusion Models [51.21351775178525]
  DiffExplainer is a novel framework that, leveraging language-vision models, enables multimodal global explainability. It employs diffusion models conditioned on optimized text prompts, synthesizing images that maximize class outputs. The analysis of generated visual descriptions allows for automatic identification of biases and spurious features.
  arXiv  Detail & Related papers  (2024-04-03T10:11:22Z)
• Text-Guided Face Recognition using Multi-Granularity Cross-Modal Contrastive Learning [0.0]
  We introduce text-guided face recognition (TGFR) to analyze the impact of integrating facial attributes in the form of natural language descriptions. TGFR demonstrates remarkable improvements, particularly on low-quality images, over existing face recognition models.
  arXiv  Detail & Related papers  (2023-12-14T22:04:22Z)
• Interactive Natural Language Processing [67.87925315773924]
  Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
  arXiv  Detail & Related papers  (2023-05-22T17:18:29Z)
• Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognition [12.677143408225167]
  We propose a semi-supervised learning framework that utilizes unlabeled face data to train expression recognition models effectively. Our method uses a dynamic threshold module that can adaptively adjust the confidence threshold to fully utilize the face recognition data. In the ABAW5 EXPR task, our method achieved excellent results on the official validation set.
  arXiv  Detail & Related papers  (2023-03-15T13:43:06Z)
• Face-to-Face Contrastive Learning for Social Intelligence Question-Answering [55.90243361923828]
  multimodal methods have set the state of the art on many tasks, but have difficulty modeling the complex face-to-face conversational dynamics. We propose Face-to-Face Contrastive Learning (F2F-CL), a graph neural network designed to model social interactions. We experimentally evaluated the challenging Social-IQ dataset and show state-of-the-art results.
  arXiv  Detail & Related papers  (2022-07-29T20:39:44Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction [17.000283696243564]
  We investigate an interactive semantic parsing framework that explains the predicted logical form step by step in natural language. We focus on question answering over knowledge bases (KBQA) as an instantiation of our framework. Our experiments show that the interactive framework with human feedback has the potential to greatly improve overall parse accuracy.
  arXiv  Detail & Related papers  (2021-10-15T20:11:22Z)
• Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition [59.29808528182607]
  Cross-resolution face recognition (CRFR) is important in intelligent surveillance and biometric forensics. Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space. In this study, we desire to fully exploit the multi-level deep convolutional neural network (CNN) feature set for robust CRFR.
  arXiv  Detail & Related papers  (2021-03-25T14:03:42Z)
• A Multi-resolution Approach to Expression Recognition in the Wild [9.118706387430883]
  We propose a multi-resolution approach to solve the Facial Expression Recognition task. We ground our intuition on the observation that often faces images are acquired at different resolutions. To our aim, we use a ResNet-like architecture, equipped with Squeeze-and-Excitation blocks, trained on the Affect-in-the-Wild 2 dataset.
  arXiv  Detail & Related papers  (2021-03-09T21:21:02Z)
• Joint Deep Learning of Facial Expression Synthesis and Recognition [97.19528464266824]
  We propose a novel joint deep learning of facial expression synthesis and recognition method for effective FER. The proposed method involves a two-stage learning procedure. Firstly, a facial expression synthesis generative adversarial network (FESGAN) is pre-trained to generate facial images with different facial expressions. In order to alleviate the problem of data bias between the real images and the synthetic images, we propose an intra-class loss with a novel real data-guided back-propagation (RDBP) algorithm.
  arXiv  Detail & Related papers  (2020-02-06T10:56:00Z)

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.