Article

Human face recognition by adaptive processing of tree structures representation

Neural Computing and Applications (impact factor: 0.7). 04/2012; 17(3):201-215. DOI:10.1007/s00521-007-0108-8 pp.201-215
Source: DBLP

ABSTRACT This paper describes a novel method of facial representation and recognition based upon adaptive processing of tree structures.
Instead of the conventional flat vector representation for a face, a neural network approach-based technique is proposed to
transform the Localised Gabor Feature (LGF) vectors extracted from human facial components into Human Face Tree Structure
(HFTS) to represent a human face. A structural training algorithm is assigned to train and recognize the face identity in
this HFTS representation with the corresponding LGF vectors. By benchmarking using the tested public face databases presented
in this paper, our approach is able to achieve accuracy up to 90% under different scenarios of lighting conditions and posture
orientations.

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Keywords

conventional flat vector representation
 
corresponding LGF vectors
 
different scenarios
 
facial representation
 
human face
 
human facial components
 
LGF
 
Localised Gabor Feature
 
neural network approach-based technique
 
novel method
 
structural training algorithm
 
tested public face databases
 

Siu-Yeung Cho