From Age Estimation to Age-Invariant Face Recognition: Generalized Age Feature Extraction Using Order-Enhanced Contrastive Learning

• URL: http://arxiv.org/abs/2501.01760v1
• Date: Fri, 03 Jan 2025 11:23:52 GMT
• Title: From Age Estimation to Age-Invariant Face Recognition: Generalized Age Feature Extraction Using Order-Enhanced Contrastive Learning
• Authors: Haoyi Wang, Victor Sanchez, Chang-Tsun Li, Nathan Clarke,
• Abstract summary: Generalized age feature extraction is crucial for age-related facial analysis tasks. We propose Order-Enhanced Contrastive Learning (OrdCon) to minimize the domain gap across different datasets and scenarios. We demonstrate that our proposed method achieves comparable results to state-of-the-art methods on various benchmark datasets.
• Score: 23.817867981093382
• License:
• Abstract: Generalized age feature extraction is crucial for age-related facial analysis tasks, such as age estimation and age-invariant face recognition (AIFR). Despite the recent successes of models in homogeneous-dataset experiments, their performance drops significantly in cross-dataset evaluations. Most of these models fail to extract generalized age features as they only attempt to map extracted features with training age labels directly without explicitly modeling the natural progression of aging. In this paper, we propose Order-Enhanced Contrastive Learning (OrdCon), which aims to extract generalized age features to minimize the domain gap across different datasets and scenarios. OrdCon aligns the direction vector of two features with either the natural aging direction or its reverse to effectively model the aging process. The method also leverages metric learning which is incorporated with a novel soft proxy matching loss to ensure that features are positioned around the center of each age cluster with minimum intra-class variance. We demonstrate that our proposed method achieves comparable results to state-of-the-art methods on various benchmark datasets in homogeneous-dataset evaluations for both age estimation and AIFR. In cross-dataset experiments, our method reduces the mean absolute error by about 1.38 in average for age estimation task and boosts the average accuracy for AIFR by 1.87%.

Related papers

• Relative Age Estimation Using Face Images [12.30301655447024]
  We employ a network that estimates age differences between an input image and reference images with known ages, thus refining the initial estimate. Our method explicitly models age-dependent facial variations using differential regression, yielding improved accuracy compared to conventional absolute age estimation. Our approach surpasses existing methods, achieving state-of-the-art accuracy on the MORPH II and CACD datasets.
  arXiv  Detail & Related papers  (2025-02-07T11:42:01Z)
• Age Prediction From Face Images Via Contrastive Learning [1.7705784090599048]
  We leverage readily available face datasets of different people at different ages and aim to extract age-related features using contrastive learning. Our method emphasizes relevant features while suppressing identity-related features using a combination of cosine similarity and triplet margin losses.
  arXiv  Detail & Related papers  (2023-08-23T03:43:34Z)
• General vs. Long-Tailed Age Estimation: An Approach to Kill Two Birds with One Stone [48.849311629912734]
  We propose a simple, effective, and flexible training paradigm named GLAE, which is two-fold. Our GLAE provides a surprising improvement on Morph II, reaching the lowest MAE and CMAE of 1.14 and 1.27 years, respectively.
  arXiv  Detail & Related papers  (2023-07-19T16:51:59Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• A Demographic Attribute Guided Approach to Age Estimation [4.215251065887862]
  This research makes use of auxiliary information of face attributes and proposes a new age estimation approach with an attribute guidance module. Experimental results on three public datasets of UTKFace, LAP2016 and Morph show that our proposed approach achieves superior performance compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-05-20T15:34:47Z)
• LAE : Long-tailed Age Estimation [52.5745217752147]
  We first formulate a simple standard baseline and build a much strong one by collecting the tricks in pre-training, data augmentation, model architecture, and so on. Compared with the standard baseline, the proposed one significantly decreases the estimation errors. We propose a two-stage training method named Long-tailed Age Estimation (LAE), which decouples the learning procedure into representation learning and classification.
  arXiv  Detail & Related papers  (2021-10-25T09:05:44Z)
• FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild [50.8865921538953]
  We propose a method to explicitly incorporate facial semantics into age estimation. We design a face parsing-based network to learn semantic information at different scales. We show that our method consistently outperforms all existing age estimation methods.
  arXiv  Detail & Related papers  (2021-06-21T14:31:32Z)
• Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation [65.5880700862751]
  We analyze the essential relationship between two state-of-the-art methods (Ranking-CNN and DLDL) and show that the Ranking method is in fact learning label distribution implicitly. We propose a lightweight network architecture and propose a unified framework which can jointly learn facial attribute distribution and regress attribute value. Our method achieves new state-of-the-art results using the single model with 36$times$ fewer parameters and 3$times$ faster inference speed on facial age/attractiveness estimation.
  arXiv  Detail & Related papers  (2020-07-03T15:46:53Z)
• Enhancing Facial Data Diversity with Style-based Face Aging [59.984134070735934]
  In particular, face datasets are typically biased in terms of attributes such as gender, age, and race. We propose a novel, generative style-based architecture for data augmentation that captures fine-grained aging patterns. We show that the proposed method outperforms state-of-the-art algorithms for age transfer.
  arXiv  Detail & Related papers  (2020-06-06T21:53:44Z)

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.