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Related Experiment Video

Updated: Oct 22, 2025

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

Evaluation of Automatic Facial Wrinkle Detection Algorithms.

Remah Mutasim Elbashir¹, Moi Hoon Yap²

¹College of Computer Science and Information Technology, Sudan University of Science and Technology, Khartoum 1111, Sudan.

Journal of Imaging

|August 30, 2021

Summary

This summary is machine-generated.

This study introduces an improved method for detecting all facial wrinkles, including vertical and horizontal lines. The enhanced technique significantly outperforms existing methods for facial wrinkle analysis.

Keywords:

FERET dataset Jaccard Similarity Index Sudanese dataset automatic wrinkle detection facial wrinkles

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Published on: December 24, 2015

Area of Science:

Computer Vision
Biometrics
Dermatology

Background:

Facial wrinkles are natural signs of aging and crucial for applications like age estimation and soft biometrics.
Current wrinkle detection algorithms primarily focus on horizontal forehead lines, neglecting comprehensive facial wrinkle analysis.
There is a need for advanced methods capable of detecting both vertical and horizontal wrinkles across the entire face.

Purpose of the Study:

To evaluate existing wrinkle detection algorithms on whole-face images.
To propose and validate an enhancement technique for improving facial wrinkle detection performance.
To address the limitations of current methods by enabling detection of all wrinkle types on the entire face.

Main Methods:

Utilized 45 images from the Face Recognition Technology (FERET) dataset and 25 from a Sudanese dataset.
Performed manual ground truth annotation for all selected facial images.
Implemented and compared an enhancement technique against state-of-the-art methods like Hybrid Hessian Filter and Gabor Filter.

Main Results:

The proposed enhancement technique demonstrated superior performance in facial wrinkle detection.
Evaluated on the FERET dataset, the enhancement method achieved an average Jaccard similarity index of 56.17%.
This significantly surpassed the performance of the Hybrid Hessian Filter (31.69%) and Gabor Filter (15.87%).

Conclusions:

The developed enhancement technique offers a significant improvement for whole-face wrinkle detection.
This advancement is crucial for more accurate facial analysis in age estimation and biometric applications.
The study highlights the effectiveness of the proposed method over traditional approaches for comprehensive facial wrinkle identification.