Title: Attention mechanism-based facial age estimation

Authors: Huiying Zhang; Jiayan Lin; Wenshun Sheng; Jiangwei Dong; Yu Zhang; Xin Geng; Deyin Zhang; Xin Jin

Addresses: Pujiang Institute, Nanjing Tech University, Nanjing, China ' Pujiang Institute, Nanjing Tech University, Nanjing, China ' Pujiang Institute, Nanjing Tech University, Nanjing, China ' School of Computer Science and Engineering, Southeast University, Nanjing, China ' School of Computer Science and Engineering, Southeast University, Nanjing, China ' School of Computer Science and Engineering, Southeast University, Nanjing, China ' Pujiang Institute, Nanjing Tech University, Nanjing, China ' School of Art and Design, Bengbu University, Anhui, China

Abstract: With the development of the deep learning (DL) technique, especially long short-term memory (LSTM) for personal ageing patterns, the accuracy of facial age estimation has been significantly improved. However, in traditional DL framework, the interdependence between individual facial images has not been fully exploited. To improve the estimation accuracy further, we propose an attention mechanism-based face ageing estimation (AM-FAE) to characterise such meaningful interdependence. The proposed AM-FAE is able to select the most relevant parts of the input and assigns different weights to different contextual face information, thereby can achieve high value of information. Compared to state-of-the-art facial age estimation methods, AM-FAE improves the accuracy of age estimation on two public datasets.

Keywords: mechanism of attention; convolutional neural networks; CNN; label distribution learning; facial age estimation.

DOI: 10.1504/IJBIC.2025.148390

International Journal of Bio-Inspired Computation, 2025 Vol.26 No.1, pp.1 - 9

Received: 25 Jul 2022
Accepted: 21 Apr 2023
Published online: 03 Sep 2025 *

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