Paper Info
Title
Robust gender classification using a precise patch histogram
Abstract
This study proposed a precise facial feature extraction method to improve the accuracy of gender classification under pose and illumination variations. We used the active appearance model (AAM) to align the face image. Images were modeled by the patches around the coordinates of certain landmarks. Using the proposed precise patch histogram (PPH) enabled us to improve the accuracy of the global facial features. The system is composed of three phases. In the training phase, non-parametric statistics were used to describe the characteristics of the training images and to construct the patch library. In the inference phase, the choice of feature patch from the library needed to approximate the patch of the testing image was based on the maximum a posteriori estimation. In the estimation phase, a Bayesian framework with portion-oriented posteriori fine-tuning was employed to determine the classification decision. In addition, we developed the dynamic weight adaptation to obtain a more convincing performance. The experimental results demonstrated the robustness of the proposed method.
Year
DOI
Venue
2013
10.1016/j.patcog.2012.08.003
Pattern Recognition
Keywords
Field
DocType
gender classification,face image,classification decision,estimation phase,inference phase,training phase,feature patch,patch library,robust gender classification,proposed precise patch histogram,bayesian classifier,face recognition,active appearance model,human computer interaction
Histogram,Eigenface,Computer science,Robustness (computer science),Artificial intelligence,Cluster analysis,Facial recognition system,Computer vision,Pattern recognition,Active appearance model,Feature extraction,Maximum a posteriori estimation,Machine learning
Journal
Volume
Issue
ISSN
46
2
0031-3203
Citations 
PageRank 
References 
10
0.47
17
Authors
1
Name
Order
Citations
PageRank
Huang-Chia Shih118721.98