Title
Pyramid multi-level features for facial demographic estimation.
Abstract
The proposed approach can run in real-time applications.The use of specific hierarchical order for demographic estimation.Several public databases are used to assess the advantages of the approach.The advantages of Pyramid Multi-Level face representation. We present a novel learning system for human demographic estimation in which the ethnicity, gender and age attributes are estimated from facial images. The proposed approach consists of the following three main stages: 1) face alignment and preprocessing; 2) constructing a Pyramid Multi-Level face representation from which the local features are extracted from the blocks of the whole pyramid; 3) feeding the obtained features to an hierarchical estimator having three layers. Due to the fact that ethnicity is by far the easiest attribute to estimate, the adopted hierarchy is as follows. The first layer predicts ethnicity of the input face. Based on that prediction, the second layer estimates the gender using the corresponding gender classifier. Based on the predicted ethnicity and gender, the age is finally estimated using the corresponding regressor.Experiments are conducted on five public databases (MORPH II, PAL, IoG, LFW and FERET) and another two challenge databases (Apparent age; Smile and Gender) of the 2016 ChaLearn Looking at People and Faces of the World Challenge and Workshop. These experiments show stable and good results. We present many comparisons against state-of-the-art methods. We also provide a study about cross-database evaluation. We quantitatively measure the performance drop in age estimation and in gender classification when the ethnicity attribute is misclassified.
Year
DOI
Venue
2017
10.1016/j.eswa.2017.03.030
Expert Syst. Appl.
Keywords
Field
DocType
Demographic estimation,Classification,Age,Gender,Ethnicity
Data mining,FERET,Computer science,Apparent age,Preprocessor,Artificial intelligence,Pyramid,Classifier (linguistics),Hierarchy,Machine learning,Estimator
Journal
Volume
Issue
ISSN
80
C
0957-4174
Citations 
PageRank 
References 
10
0.60
36
Authors
5
Name
Order
Citations
PageRank
salah eddine bekhouche1323.64
abdelkrim ouafi2414.88
Fadi Dornaika380996.43
Abdelmalik Taleb-ahmed49622.55
Abdenour Hadid53305146.00