Authors:
Mohamed Selim
1
;
Shekhar Raheja
2
and
Didier Stricker
3
Affiliations:
1
German Research Center for Artificial Intelligence and DFKI, Germany
;
2
Technical University Kaiserslautern, Germany
;
3
DFKI, German Research Center for Artificial Intelligence and DFKI, Germany
Keyword(s):
Age-group Estimation, Local Binary Patterns, Extended Local Binary Patterns, Real-time, K-Nearest Neighbours.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
This paper summarizes work done on real-time human age-group estimation based on frontal facial images.
Our approach relies on detecting visible ageing effects, such as facial skin texture. This information is described
using uniform Local Binary Patterns (LBP) and the estimation is done using the K-Nearest Neighbour
classifier. In the current work, the system is trained using the FERET dataset. The training data is divided
into five main age groups. Facial images captured in real-time using the Microsoft Kinect RGB data are used
to classify the subjects age into one of the five different age groups. An accuracy of 81% was achieved on
the live testing data. In the proposed approach, only facial regions affected by the ageing process are used
in the face description. Moreover, the use of uniform Local Binary Patterns is evaluated in the context of
facial description and age-group estimation. Results show that the uniform LBP depicts most of the facial
texture information. That led to
speeding up the entire process as the feature vector’s length has been reduced
significantly, which optimises the process for real-time applications.
(More)