A new approach to face localization in the HSV space using the Gaussian model

Authors:
Mohamed Deriche;Imran Naseem
Affiliations:
Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran,;Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran,
Venue:
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Year:
2007

Citing 12
Cited 0

Automatic extraction of face-features

Pattern Recognition Letters
Automatic recognition and analysis of human faces and facial expressions: a survey

Pattern Recognition
Feature extraction from faces using deformable templates

International Journal of Computer Vision
Machine vision

Machine vision
Locating human faces in photographs

International Journal of Computer Vision
Probabilistic Visual Learning for Object Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Finding Face Features

ECCV '92 Proceedings of the Second European Conference on Computer Vision
Edge and Keypoint Detection in Facial Regions

FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
FloatBoost Learning and Statistical Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to identify video shots with people based on face detection

ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a model based approach for the problem of face localization. Traditionally, images are represented in the RGB color space, which is a 3-dimensional space that includes the illumination factor. However, the human skin color of different ethnic groups has been shown to change because of brightness. We therefore propose to transform the RGB images into the HSV color-space. We then exclude the V component, and use the HS-domain to represent skin pixels using a Gaussian probability model. The model is used to obtain a skin likelihood image which is further transformed into a binary image using the fuzzy C-mean clustering (FCM) technique. The candidate skin regions are checked for some facial properties and finally a template face matching approach is used to localize the face.. The developed algorithm is found robust and reliable under various imaging conditions and even in the presence of structural objects like hairs, spectacles, etc.