Feature Representation in Deep Metric Embeddings

• URL: http://arxiv.org/abs/2102.03176v2
• Date: Fri, 31 Mar 2023 14:27:39 GMT
• Title: Feature Representation in Deep Metric Embeddings
• Authors: Ryan Furlong, Vincent O'Brien, James Garland, Daniel Palacios-Alonso, Francisco Dominguez-Mateos
• Abstract summary: This study takes embeddings trained to discriminate faces (identities) and uses unsupervised clustering to identify the features involved in facial identity discrimination. In the intra class scenario, the inference process distinguishes common attributes between single identities, achieving 90.0% and 76.0% accuracy for beards and glasses, respectively. The system can also perform extra class sub-discrimination with a high accuracy rate, notably 99.3%, 99.3% and 94.1% for gender, skin tone, and age, respectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In deep metric learning (DML), high-level input data are represented in a lower-level representation (embedding) space, such that samples from the same class are mapped close together, while samples from disparate classes are mapped further apart. In this lower-level representation, only a single inference sample from each known class is required to discriminate between classes accurately. The features a DML model uses to discriminate between classes and the importance of each feature in the training process are unknown. To investigate this, this study takes embeddings trained to discriminate faces (identities) and uses unsupervised clustering to identify the features involved in facial identity discrimination by examining their representation within the embedded space. This study is split into two cases; intra class sub-discrimination, where attributes that differ between a single identity are considered; such as beards and emotions; and extra class sub-discrimination, where attributes which differ between different identities/people, are considered; such as gender, skin tone and age. In the intra class scenario, the inference process distinguishes common attributes between single identities, achieving 90.0\% and 76.0\% accuracy for beards and glasses, respectively. The system can also perform extra class sub-discrimination with a high accuracy rate, notably 99.3\%, 99.3\% and 94.1\% for gender, skin tone, and age, respectively.

Related papers

• LabellessFace: Fair Metric Learning for Face Recognition without Attribute Labels [0.11999555634662631]
  This paper introduces LabellessFace'', a framework that improves demographic bias in face recognition without requiring demographic group labeling. We propose a novel fairness enhancement metric called the class favoritism level, which assesses the extent of favoritism towards specific classes. This method dynamically adjusts learning parameters based on class favoritism levels, promoting fairness across all attributes.
  arXiv  Detail & Related papers  (2024-09-14T02:56:07Z)
• FineFACE: Fair Facial Attribute Classification Leveraging Fine-grained Features [3.9440964696313485]
  Research highlights the presence of demographic bias in automated facial attribute classification algorithms. Existing bias mitigation techniques typically require demographic annotations and often obtain a trade-off between fairness and accuracy. This paper proposes a novel approach to fair facial attribute classification by framing it as a fine-grained classification problem.
  arXiv  Detail & Related papers  (2024-08-29T20:08:22Z)
• High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning [54.86882315023791]
  We propose an innovative approach called High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning (HDAFL) HDAFL utilizes multiple convolutional kernels to automatically learn discriminative regions highly correlated with attributes in images. We also introduce a Transformer-based attribute discrimination encoder to enhance the discriminative capability among attributes.
  arXiv  Detail & Related papers  (2024-04-07T13:17:47Z)
• Leveraging vision-language models for fair facial attribute classification [19.93324644519412]
  General-purpose vision-language model (VLM) is a rich knowledge source for common sensitive attributes. We analyze the correspondence between VLM predicted and human defined sensitive attribute distribution. Experiments on multiple benchmark facial attribute classification datasets show fairness gains of the model over existing unsupervised baselines.
  arXiv  Detail & Related papers  (2024-03-15T18:37:15Z)
• 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)
• Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection using Adversarial Learning [59.548960057358435]
  We propose a novel method which utilizes adversarial learning to eliminate the negative effect of different forgery methods and facial identities. Our face forgery detection model learns to extract common discriminative features through eliminating the effect of forgery methods and facial identities.
  arXiv  Detail & Related papers  (2022-07-08T09:23:59Z)
• MultiFair: Multi-Group Fairness in Machine Learning [52.24956510371455]
  We study multi-group fairness in machine learning (MultiFair) We propose a generic end-to-end algorithmic framework to solve it. Our proposed framework is generalizable to many different settings.
  arXiv  Detail & Related papers  (2021-05-24T02:30:22Z)
• Learning and Evaluating Representations for Deep One-class Classification [59.095144932794646]
  We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations. In experiments, we demonstrate state-of-the-art performance on visual domain one-class classification benchmarks.
  arXiv  Detail & Related papers  (2020-11-04T23:33:41Z)
• Representative-Discriminative Learning for Open-set Land Cover Classification of Satellite Imagery [11.47389428456188]
  We study the problem of open-set land cover classification that identifies the samples belonging to unknown classes during testing. Although inherently a classification problem, both representative and discriminative aspects of data need to be exploited. We propose a representative-discriminative open-set recognition framework.
  arXiv  Detail & Related papers  (2020-07-21T15:28:56Z)
• Mitigating Face Recognition Bias via Group Adaptive Classifier [53.15616844833305]
  This work aims to learn a fair face representation, where faces of every group could be more equally represented. Our work is able to mitigate face recognition bias across demographic groups while maintaining the competitive accuracy.
  arXiv  Detail & Related papers  (2020-06-13T06:43:37Z)

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.