Article: On age prediction from facial images in presence of facial expressions Journal: International Journal of Applied Pattern Recognition (IJAPR) 2021 Vol.6 No.4 pp.345 - 369 Abstract: Predicting age and gender from facial images is a fundamental research problem that has many applications in major research areas. The state-of-the-art online APIs can predict age and gender but their accuracy degrades when emotions are present. In this paper, we present feature-extraction based machine learning models that can predict ages with acceptable accuracy in presence of facial-expressions. After identifying 68 facial landmarks, different distances and ratios (that changes with age and expressions) are selected to predict the age that can overcome the impact of emotions with reasonable accuracy. The experimental results show that while neutralising the effect of emotion, the proposed models can perform better on female images compared to the male image set. And images with disgust and contempt expressions deviate most during prediction. In contrast, predicted age is more accurate for angry expressions. Also for different ethnic groups, the predicted age deviates differently from the actual age. Inderscience Publishers - linking academia, business and industry through research

Title: On age prediction from facial images in presence of facial expressions

Authors: Md. Shahedul Haque Shawon; Sagor Biswas; Nirob Arefin; Tanmoy Sarkar Pias; Ashikur Rahman

Addresses: Department of Computer Science and Engineering (CSE), Bangladesh University of Engineering and Technology, Dhaka – 1000, Bangladesh ' Department of Computer Science and Engineering (CSE), Bangladesh University of Engineering and Technology, Dhaka – 1000, Bangladesh ' Department of Computer Science and Engineering (CSE), Bangladesh University of Engineering and Technology, Dhaka – 1000, Bangladesh ' Department of Computer Science (CS), Virginia Tech, Blacksburg, VA 24060, USA ' Department of Computer Science and Engineering (CSE), Bangladesh University of Engineering and Technology, Dhaka – 1000, Bangladesh

Abstract: Predicting age and gender from facial images is a fundamental research problem that has many applications in major research areas. The state-of-the-art online APIs can predict age and gender but their accuracy degrades when emotions are present. In this paper, we present feature-extraction based machine learning models that can predict ages with acceptable accuracy in presence of facial-expressions. After identifying 68 facial landmarks, different distances and ratios (that changes with age and expressions) are selected to predict the age that can overcome the impact of emotions with reasonable accuracy. The experimental results show that while neutralising the effect of emotion, the proposed models can perform better on female images compared to the male image set. And images with disgust and contempt expressions deviate most during prediction. In contrast, predicted age is more accurate for angry expressions. Also for different ethnic groups, the predicted age deviates differently from the actual age.

Keywords: age estimation; facial expression; API; feature; machine learning; support vector regression; SVR; facial landmark; feature engineering; CK+.

DOI: 10.1504/IJAPR.2021.118918

International Journal of Applied Pattern Recognition, 2021 Vol.6 No.4, pp.345 - 369

Received: 27 Apr 2021
Accepted: 12 Jul 2021
Published online: 11 Nov 2021 *

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Title: On age prediction from facial images in presence of facial expressions

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