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Updated: Apr 25, 2026

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Published on: December 12, 2012

A modified active appearance model based on an adaptive artificial bee colony.

Mohammed Hasan Abdulameer¹, Siti Norul Huda Sheikh Abdullah², Zulaiha Ali Othman³

¹Pattern Recognition Research Group, Centre for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bandar Baru Bangi, Malaysia ; Department of Computer Science, Faculty of Education for Women, University of Kufa, Iraq.

Thescientificworldjournal

|August 29, 2014

Summary

This study introduces an adaptive ABC algorithm to improve Active Appearance Model (AAM) fitting for face recognition. The new method enhances fitting efficiency and recognition accuracy across multiple datasets.

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Area of Science:

Computer Vision
Biometrics
Machine Learning

Background:

Active Appearance Models (AAM) are widely used for feature extraction in face recognition.
Fitting AAMs to images presents a significant challenge due to complex variations.
Existing optimization methods offer solutions but face their own application difficulties.

Purpose of the Study:

To propose a novel Active Appearance Model (AAM) based face recognition technique.
To address the AAM fitting problem by introducing an adaptive Artificial Bee Colony (ABC) algorithm.
To enhance the efficiency and accuracy of face recognition.

Main Methods:

Developed a new adaptive ABC algorithm tailored for AAM fitting.
Integrated the adaptive ABC algorithm into an AAM-based face recognition framework.
Evaluated the technique on CASIA, 2.5D face, and UBIRIS v1 datasets.

Main Results:

The proposed adaptive ABC algorithm demonstrated improved fitting efficiency compared to conventional ABC.
The AAM-based face recognition technique achieved effective performance.
Experimental results indicated enhanced accuracy in face recognition.

Conclusions:

The adaptive ABC algorithm successfully resolves the AAM fitting challenge.
The proposed method offers a more efficient and accurate approach to AAM-based face recognition.
This technique shows promise for robust face recognition applications.