Noise-Tolerance GPU-based Age Estimation Using ResNet-50
- URL: http://arxiv.org/abs/2305.00848v1
- Date: Wed, 26 Apr 2023 16:38:41 GMT
- Title: Noise-Tolerance GPU-based Age Estimation Using ResNet-50
- Authors: Mahtab Taheri, Mahdi Taheri, and Amirhossein Hadjahmadi
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The human face contains important and understandable information such as
personal identity, gender, age, and ethnicity. In recent years, a person's age
has been studied as one of the important features of the face. The age
estimation system consists of a combination of two modules, the presentation of
the face image and the extraction of age characteristics, and then the
detection of the exact age or age group based on these characteristics. So far,
various algorithms have been presented for age estimation, each of which has
advantages and disadvantages. In this work, we implemented a deep residual
neural network on the UTKFace data set. We validated our implementation by
comparing it with the state-of-the-art implementations of different age
estimation algorithms and the results show 28.3% improvement in MAE as one of
the critical error validation metrics compared to the recent works and also
71.39% MAE improvements compared to the implemented AlexNet. In the end, we
show that the performance degradation of our implemented network is lower than
1.5% when injecting 15 dB noise to the input data (5 times more than the normal
environmental noise) which justifies the noise tolerance of our proposed
method.
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) - SwinFace: A Multi-task Transformer for Face Recognition, Expression
Recognition, Age Estimation and Attribute Estimation [60.94239810407917]
This paper presents a multi-purpose algorithm for simultaneous face recognition, facial expression recognition, age estimation, and face attribute estimation based on a single Swin Transformer.
To address the conflicts among multiple tasks, a Multi-Level Channel Attention (MLCA) module is integrated into each task-specific analysis.
Experiments show that the proposed model has a better understanding of the face and achieves excellent performance for all tasks.
arXiv Detail & Related papers (2023-08-22T15:38:39Z) - 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) - using multiple losses for accurate facial age estimation [6.851375622634309]
We propose a simple yet effective approach for age estimation, which improves the performance compared to classification-based methods.
We validate the Age-Granularity-Net framework on the CVPR Chalearn 2016 dataset, and extensive experiments show that the proposed approach can reduce the prediction error compared to any individual loss.
arXiv Detail & Related papers (2021-06-17T11:18:16Z) - 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) - The FaceChannel: A Fast & Furious Deep Neural Network for Facial
Expression Recognition [71.24825724518847]
Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train.
We formalize the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks.
We demonstrate how our model achieves a comparable, if not better, performance to the current state-of-the-art in FER.
arXiv Detail & Related papers (2020-09-15T09:25:37Z) - 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.