A Demographic Attribute Guided Approach to Age Estimation

• URL: http://arxiv.org/abs/2205.10254v1
• Date: Fri, 20 May 2022 15:34:47 GMT
• Title: A Demographic Attribute Guided Approach to Age Estimation
• Authors: Zhicheng Cao, Kaituo Zhang, Liaojun Pang, Heng Zhao
• Abstract summary: 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.
• Score: 4.215251065887862
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Face-based age estimation has attracted enormous attention due to wide applications to public security surveillance, human-computer interaction, etc. With vigorous development of deep learning, age estimation based on deep neural network has become the mainstream practice. However, seeking a more suitable problem paradigm for age change characteristics, designing the corresponding loss function and designing a more effective feature extraction module still needs to be studied. What is more, change of face age is also related to demographic attributes such as ethnicity and gender, and the dynamics of different age groups is also quite different. This problem has so far not been paid enough attention to. How to use demographic attribute information to improve the performance of age estimation remains to be further explored. In light of these issues, this research makes full use of auxiliary information of face attributes and proposes a new age estimation approach with an attribute guidance module. We first design a multi-scale attention residual convolution unit (MARCU) to extract robust facial features other than simply using other standard feature modules such as VGG and ResNet. Then, after being especially treated through full connection (FC) layers, the facial demographic attributes are weight-summed by 1*1 convolutional layer and eventually merged with the age features by a global FC layer. Lastly, we propose a new error compression ranking (ECR) loss to better converge the age regression value. 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.

Related papers

• 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)
• Noise-Tolerance GPU-based Age Estimation Using ResNet-50 [0.0]
  We implement a deep residual neural network on the UTKFace data set. We show that the performance of our implemented network is lower than 1.5% when injecting 15 dB noise to the input data.
  arXiv  Detail & Related papers  (2023-04-26T16:38:41Z)
• 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)
• 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)
• 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)
• Continuous Face Aging via Self-estimated Residual Age Embedding [8.443742714362521]
  We propose a unified network structure that embeds a linear age estimator into a GAN-based model. The embedded age estimator is trained jointly with the encoder and decoder to estimate the age of a face image. The personalized target age embedding is synthesized by incorporating both personalized residual age embedding of the current age and exemplar-face aging basis of the target age.
  arXiv  Detail & Related papers  (2021-04-30T18:06:17Z)
• PFA-GAN: Progressive Face Aging with Generative Adversarial Network [19.45760984401544]
  This paper proposes a novel progressive face aging framework based on generative adversarial network (PFA-GAN) The framework can be trained in an end-to-end manner to eliminate accumulative artifacts and blurriness. Extensively experimental results demonstrate superior performance over existing (c)GANs-based methods.
  arXiv  Detail & Related papers  (2020-12-07T05:45:13Z)
• Age Gap Reducer-GAN for Recognizing Age-Separated Faces [72.26969872180841]
  We propose a novel algorithm for matching faces with temporal variations caused due to age progression. The proposed generative adversarial network algorithm is a unified framework that combines facial age estimation and age-separated face verification.
  arXiv  Detail & Related papers  (2020-11-11T16:43:32Z)
• 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.