The Good, the Better, and the Best: Improving the Discriminability of Face Embeddings through Attribute-aware Learning

• URL: http://arxiv.org/abs/2603.15062v1
• Date: Mon, 16 Mar 2026 10:18:35 GMT
• Title: The Good, the Better, and the Best: Improving the Discriminability of Face Embeddings through Attribute-aware Learning
• Abstract summary: A common strategy to guide representation learning with auxiliary supervision from facial attributes is to encourage the visual encoder to focus on identity-relevant regions.<n>Existing approaches typically rely on heterogeneous and fixed sets of attributes, implicitly assuming equal relevance across attributes.<n>We propose an attribute-aware face recognition architecture that supervises the learning of facial embeddings using identity class labels, identity-relevant facial attributes, and non-identity-related attributes.
• Score: 5.429555343961488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite recent advances in face recognition, robust performance remains challenging under large variations in age, pose, and occlusion. A common strategy to address these issues is to guide representation learning with auxiliary supervision from facial attributes, encouraging the visual encoder to focus on identity-relevant regions. However, existing approaches typically rely on heterogeneous and fixed sets of attributes, implicitly assuming equal relevance across attributes. This assumption is suboptimal, as different attributes exhibit varying discriminative power for identity recognition, and some may even introduce harmful biases. In this paper, we propose an attribute-aware face recognition architecture that supervises the learning of facial embeddings using identity class labels, identity-relevant facial attributes, and non-identity-related attributes. Facial attributes are organized into interpretable groups, making it possible to decompose and analyze their individual contributions in a human-understandable manner. Experiments on standard face verification benchmarks demonstrate that joint learning of identity and facial attributes improves the discriminability of face embeddings with two major conclusions: (i) using identity-relevant subsets of facial attributes consistently outperforms supervision with a broader attribute set, and (ii) explicitly forcing embeddings to unlearn non-identity-related attributes yields further performance gains compared to leaving such attributes unsupervised. Additionally, our method serves as a diagnostic tool for assessing the trustworthiness of face recognition encoders by allowing for the measurement of accuracy gains with suppression of non-identity-relevant attributes, with such gains suggesting shortcut learning from redundant attributes associated with each identity.

Related papers

• Beyond Inference Intervention: Identity-Decoupled Diffusion for Face Anonymization [55.29071072675132]
  Face anonymization aims to conceal identity information while preserving non-identity attributes.<n>We propose textbfIDsuperscript2Face, a training-centric anonymization framework.<n>We show that IDtextsuperscript2Face outperforms existing methods in visual quality, identity suppression, and utility preservation.
  arXiv  Detail & Related papers  (2025-10-28T09:28:12Z)
• Attribute Guidance With Inherent Pseudo-label For Occluded Person Re-identification [16.586742421279137]
  Attribute-Guide ReID (AG-ReID) is a novel framework to extract fine-grained semantic attributes without additional data or annotations.<n>Our framework operates through a two-stage process: first generating attribute pseudo-labels that capture subtle visual characteristics, then introducing a dual-guidance mechanism.<n>Extensive experiments demonstrate that AG-ReID achieves state-of-the-art results on multiple widely-used Re-ID datasets.
  arXiv  Detail & Related papers  (2025-08-07T03:13:24Z)
• 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)
• Disentangle Before Anonymize: A Two-stage Framework for Attribute-preserved and Occlusion-robust De-identification [55.741525129613535]
  "Disentangle Before Anonymize" is a novel two-stage Framework(DBAF)<n>This framework includes a Contrastive Identity Disentanglement (CID) module and a Key-authorized Reversible Identity Anonymization (KRIA) module.<n>Extensive experiments demonstrate that our method outperforms state-of-the-art de-identification approaches.
  arXiv  Detail & Related papers  (2023-11-15T08:59:02Z)
• Explaining Bias in Deep Face Recognition via Image Characteristics [9.569575076277523]
  We evaluate ten state-of-the-art face recognition models, comparing their fairness in terms of security and usability on two data sets. We then analyze the impact of image characteristics on models performance.
  arXiv  Detail & Related papers  (2022-08-23T17:18:23Z)
• CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition [80.07590100872548]
  We propose Contrastive Inhibitory Adaptati On (CIAO), a mechanism that adapts the last layer of facial encoders to depict specific affective characteristics on different datasets. CIAO presents an improvement in facial expression recognition performance over six different datasets with very unique affective representations.
  arXiv  Detail & Related papers  (2022-08-10T15:46:05Z)
• 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)
• Deep Collaborative Multi-Modal Learning for Unsupervised Kinship Estimation [53.62256887837659]
  Kinship verification is a long-standing research challenge in computer vision. We propose a novel deep collaborative multi-modal learning (DCML) to integrate the underlying information presented in facial properties. Our DCML method is always superior to some state-of-the-art kinship verification methods.
  arXiv  Detail & Related papers  (2021-09-07T01:34:51Z)
• Disentanglement for Discriminative Visual Recognition [7.954325638519141]
  This chapter systematically summarize the detrimental factors as task-relevant/irrelevant semantic variations and unspecified latent variation. The better FER performance can be achieved by combining the deep metric loss and softmax loss in a unified two fully connected layer branches framework. The framework achieves top performance on a serial of tasks, including lighting, makeup, disguise-tolerant face recognition and facial attributes recognition.
  arXiv  Detail & Related papers  (2020-06-14T06:10:51Z)
• An Empirical Study of Person Re-Identification with Attributes [15.473033192858543]
  In this paper, an attribute-based approach is proposed where the person of interest is described by a set of visual attributes. We compare multiple algorithms and analyze how the quality of attributes impacts the performance. A key conclusion is that the performance achieved by non-expert attributes, instead of expert-annotated ones, is a more faithful indicator of the status quo of attribute-based approaches for person re-identification.
  arXiv  Detail & Related papers  (2020-01-25T22:18:51Z)

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.