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- Title
Enhancing Age Estimation from Handwriting: A Deep Learning Approach with Attention Mechanisms.
- Authors
Li Zhao; Xiaoping Wu; Xiaoming Chen
- Abstract
Currently, age estimation is a hot research topic in the field of forensic biology. Age estimation methods based on facial or brain features are easily affected by external factors. In contrast, handwriting analysis is a more reliable method for age estimation. This paper aims to improve the accuracy and efficiency of age prediction using handwriting analysis by proposing a novel method that integrates a coordinate attention mechanism in a deep residual network (CA-ResNet). This method can more accurately capture important features in the input handwritten images while reducing the number of model parameters, thereby improving the accuracy (Acc) and efficiency of the model for age estimation. The proposed method is evaluated on standard handwriting datasets and the created dataset, and it is compared with the current state-of-the-art methods. The results show that the method consistently outperforms others, achieving an accuracy of 79.60% on the IAM handwriting dataset, with a 6.31% improvement over other methods.
- Subjects
HANDWRITING; DEEP learning; FORENSIC biology; ARTIFICIAL neural networks; PARAMETER estimation
- Publication
International Journal of Advanced Computer Science & Applications, 2024, Vol 15, Issue 5, p738
- ISSN
2158-107X
- Publication type
Article
- DOI
10.14569/ijacsa.2024.0150574