Detect and Analyze Face Parts Information using Viola- Jones and Geometric Approaches
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10.5120/17667-8494 |
Amr El Maghraby, Mahmoud Abdalla, Othman Enany and Mohamed El Y Nahas. Article: Detect and Analyze Face Parts Information using Viola- Jones and Geometric Approaches. International Journal of Computer Applications 101(3):23-28, September 2014. Full text available. BibTeX
@article{key:article,
author = {Amr El Maghraby and Mahmoud Abdalla and Othman Enany and Mohamed Y. El Nahas},
title = {Article: Detect and Analyze Face Parts Information using Viola- Jones and Geometric Approaches},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {101},
number = {3},
pages = {23-28},
month = {September},
note = {Full text available}
}
Abstract
This paper presents a new face parts information analyzer, as a promising model for detecting faces and locating the facial features in images. The main objective is to build fully automated human facial measurements systems from images with complex backgrounds. Detection of facial features such as eye, nose, and mouth is an important step for many subsequent facial image analysis tasks. The study covers the tasks detection, landmark localization and measurement facial part that have traditionally been approached as separate problems with different techniques. Different set of techniques have been introduced recently, for example; principal component analysis, geometric modeling, auto-correlation, deformable template, neural networks, color analysis, window classifiers, view-based Eigen space methods, and elastic graph models. The study present a novel and simple model approach based on a mixture of techniques and algorithms in a shared pool based on Viola–Jones object detection framework algorithm combined with geometric and symmetric information of the face parts from the image in a smart algorithm. The study is a continued part of previous work [1] the proposed model is modestly applied with hundreds of face images taken under different lighting conditions, a number of general assumptions used in this research field are identified. After analyzing and testing the new algorithm with hundreds of faces, several promising directions for future research are concluded.
References
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