Title :
Parameter prediction for RIU-LBP based on PSO-BP algorithm
Author :
Tan, Ying ; Fang, Yuchun ; Cheng, Gong ; Dai, Wang
Author_Institution :
Sch. of Comput. Eng. & Sci., Shanghai Univ., Shanghai, China
Abstract :
Local Binary Pattern (LBP) is one of the most popular feature extraction algorithms in face recognition with good performance. However, setting proper parameters for this algorithm is still an open question in pattern recognition. In most previous research, this problem is solved with experienced comparison tests. However, such tests might be constrained by certain database and application and thus lack of generalization ability. In this paper, based on our previous research on factor analysis of the Rotation Invariant Uniform LBP (RIU-LBP) feature, we propose a parameter prediction and selection method based on the Particle Swarm Optimizer-Back Propagation neural network (PSO-BP) for setting the dominant factor, i.e. the blocking number for RIU-LBP feature. Experimental results show that the proposed prediction method could effectively save the computation time in parameter selection.
Keywords :
backpropagation; face recognition; feature extraction; neural nets; particle swarm optimisation; PSO-BP algorithm; dominant factor; face recognition; factor analysis; feature extraction algorithm; local binary pattern; parameter prediction; parameter selection; particle swarm optimizer-back propagation neural network; pattern recognition; rotation invariant uniform; selection method; Algorithm design and analysis; Biological neural networks; Face recognition; Image resolution; Particle swarm optimization; Prediction algorithms; Training; BP neural network; Local binary pattern; Parameter prediction; Particle Swarm Optimizer;
Conference_Titel :
Image and Signal Processing (CISP), 2011 4th International Congress on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9304-3
DOI :
10.1109/CISP.2011.6100434