A Hybrid Transformer-Sequencer approach for Age and Gender classification from in-wild facial images

• URL: http://arxiv.org/abs/2403.12483v2
• Date: Wed, 20 Mar 2024 07:56:29 GMT
• Title: A Hybrid Transformer-Sequencer approach for Age and Gender classification from in-wild facial images
• Authors: Aakash Singh, Vivek Kumar Singh,
• Abstract summary: This paper proposes a hybrid model that combines self-attention and BiLSTM approaches for age and gender classification problems. An improvement of approximately 10percent and 6percent over the state-of-the-art implementations for age and gender classification, respectively, are noted for the proposed model.
• Score: 1.7556999242499645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The advancements in computer vision and image processing techniques have led to emergence of new application in the domain of visual surveillance, targeted advertisement, content-based searching, and human-computer interaction etc. Out of the various techniques in computer vision, face analysis, in particular, has gained much attention. Several previous studies have tried to explore different applications of facial feature processing for a variety of tasks, including age and gender classification. However, despite several previous studies having explored the problem, the age and gender classification of in-wild human faces is still far from the achieving the desired levels of accuracy required for real-world applications. This paper, therefore, attempts to bridge this gap by proposing a hybrid model that combines self-attention and BiLSTM approaches for age and gender classification problems. The proposed models performance is compared with several state-of-the-art model proposed so far. An improvement of approximately 10percent and 6percent over the state-of-the-art implementations for age and gender classification, respectively, are noted for the proposed model. The proposed model is thus found to achieve superior performance and is found to provide a more generalized learning. The model can, therefore, be applied as a core classification component in various image processing and computer vision problems.

Related papers

• Evaluating Multiview Object Consistency in Humans and Image Models [68.36073530804296]
  We leverage an experimental design from the cognitive sciences which requires zero-shot visual inferences about object shape. We collect 35K trials of behavioral data from over 500 participants. We then evaluate the performance of common vision models.
  arXiv  Detail & Related papers  (2024-09-09T17:59:13Z)
• 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)
• MiVOLO: Multi-input Transformer for Age and Gender Estimation [0.0]
  We present MiVOLO, a straightforward approach for age and gender estimation using the latest vision transformer. Our method integrates both tasks into a unified dual input/output model. We compare our model's age recognition performance with human-level accuracy and demonstrate that it significantly outperforms humans across a majority of age ranges.
  arXiv  Detail & Related papers  (2023-07-10T14:58:10Z)
• Human Image Generation: A Comprehensive Survey [44.204029557298476]
  In this paper, we divide human image generation techniques into three paradigms, i.e., data-driven methods, knowledge-guided methods and hybrid methods. The advantages and characteristics of different methods are summarized in terms of model architectures. Due to the wide application potentials, the typical downstream usages of synthesized human images are covered.
  arXiv  Detail & Related papers  (2022-12-17T15:19:45Z)
• Are Commercial Face Detection Models as Biased as Academic Models? [64.71318433419636]
  We compare academic and commercial face detection systems, specifically examining robustness to noise. We find that state-of-the-art academic face detection models exhibit demographic disparities in their noise robustness. We conclude that commercial models are always as biased or more biased than an academic model.
  arXiv  Detail & Related papers  (2022-01-25T02:21:42Z)
• Facial Information Analysis Technology for Gender and Age Estimation [0.0]
  Gender classification was relatively simple compared to age estimation, and age estimation was made possible using deep learning-based facial recognition technology. Deep learning-based gender classification and age estimation performed at a significant level and was more robust to environmental changes compared to the existing machine learning techniques.
  arXiv  Detail & Related papers  (2021-11-17T18:56:43Z)
• 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)
• Enhance Gender and Identity Preservation in Face Aging Simulation for Infants and Toddlers [10.447210000352847]
  We propose a new deep learning method inspired by the Conditional Adversarial Autoencoder (CAAE, 2017) model. We trained our model using the publicly available UTKFace dataset and evaluated our model by simulating up to 100 years of aging on 1,156 male and 1,207 female infant and toddler face photos.
  arXiv  Detail & Related papers  (2020-11-15T01:40:36Z)
• 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)
• Age and Gender Prediction From Face Images Using Attentional Convolutional Network [6.3344832182228]
  We propose a deep learning framework, based on the ensemble of attentional and residual convolutional networks, to predict gender and age group of facial images with high accuracy rate. Our model is trained on a popular face age and gender dataset, and achieved promising results.
  arXiv  Detail & Related papers  (2020-10-08T06:33:55Z)
• Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study [67.3961439193994]
  We introduce the most comprehensive, large-scale dataset of facial images and videos to date. The data are manually annotated in terms of identity, exact age, gender and kinship. A method to debias network embeddings is introduced and tested on the proposed benchmarks.
  arXiv  Detail & Related papers  (2020-05-15T00:14:39Z)

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.