Hierarchical Attention-based Age Estimation and Bias Estimation
- URL: http://arxiv.org/abs/2103.09882v2
- Date: Wed, 27 Sep 2023 21:26:08 GMT
- Title: Hierarchical Attention-based Age Estimation and Bias Estimation
- Authors: Shakediel Hiba and Yosi Keller
- Abstract summary: We propose a novel deep-learning approach for age estimation based on face images.
Our scheme is shown to outperform contemporary schemes and provide a new state-of-the-art age estimation accuracy.
- Score: 16.335191345543063
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this work we propose a novel deep-learning approach for age estimation
based on face images. We first introduce a dual image augmentation-aggregation
approach based on attention. This allows the network to jointly utilize
multiple face image augmentations whose embeddings are aggregated by a
Transformer-Encoder. The resulting aggregated embedding is shown to better
encode the face image attributes. We then propose a probabilistic hierarchical
regression framework that combines a discrete probabilistic estimate of age
labels, with a corresponding ensemble of regressors. Each regressor is
particularly adapted and trained to refine the probabilistic estimate over a
range of ages. Our scheme is shown to outperform contemporary schemes and
provide a new state-of-the-art age estimation accuracy, when applied to the
MORPH II dataset for age estimation. Last, we introduce a bias analysis of
state-of-the-art age estimation results.
Related papers
- CILF-CIAE: CLIP-driven Image-Language Fusion for Correcting Inverse Age Estimation [14.639340916340801]
The age estimation task aims to predict the age of an individual by analyzing facial features in an image.
Existing CLIP-based age estimation methods require high memory usage and lack an error feedback mechanism.
We propose a novel CLIP-driven Image-Language Fusion for Correcting Inverse Age Estimation (CILF-CIAE)
arXiv Detail & Related papers (2023-12-04T09:35:36Z) - Content Bias in Deep Learning Image Age Approximation: A new Approach Towards better Explainability [4.088355251010862]
In temporal image forensics, content bias can be exploited by a neural network.
A novel approach is proposed that evaluates the influence of image content.
It is shown that a deep learning approach proposed in the context of age classification is most likely highly dependent on the image content.
arXiv Detail & Related papers (2023-10-03T14:09:27Z) - PDC-Net+: Enhanced Probabilistic Dense Correspondence Network [161.76275845530964]
Enhanced Probabilistic Dense Correspondence Network, PDC-Net+, capable of estimating accurate dense correspondences.
We develop an architecture and an enhanced training strategy tailored for robust and generalizable uncertainty prediction.
Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets.
arXiv Detail & Related papers (2021-09-28T17:56:41Z) - 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) - Reducing Racial Bias in Facial Age Prediction using Unsupervised Domain
Adaptation in Regression [0.0]
We propose an approach for unsupervised domain adaptation for the task of estimating someone's age from a given face image.
In order to avoid the propagation of racial bias in most publicly available face image datasets, we perform domain adaptation to motivate the predictor to learn features that are invariant to ethnicity.
arXiv Detail & Related papers (2021-04-05T05:31:12Z) - Confidence Adaptive Anytime Pixel-Level Recognition [86.75784498879354]
Anytime inference requires a model to make a progression of predictions which might be halted at any time.
We propose the first unified and end-to-end model approach for anytime pixel-level recognition.
arXiv Detail & Related papers (2021-04-01T20:01:57Z) - 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) - Convolutional Ordinal Regression Forest for Image Ordinal Estimation [52.67784321853814]
We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation.
The proposed CORF integrates ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships.
The effectiveness of the proposed CORF is verified on two image ordinal estimation tasks, showing significant improvements and better stability over the state-of-the-art ordinal regression methods.
arXiv Detail & Related papers (2020-08-07T10:41:17Z) - 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)
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.