Local Accuracy Measurement for Face Recognition System using Numerous Classifier (PCA, GA and ANN)

Between the various biometric methods, Face Recognition has become one of the most burning topic tasks in the pattern recognition field during the past decades. In This Work a Face Recognition System has been developed By applying different multiple classifier selection schemes on the output of three different classification methods namely Artificial Neural Network, Genetic Algorithm And Euclidean distance measure based on Principal Component Analysis dimensionality reduction technique. Dynamic classifier selection technique using classifier of local accuracy measurement is to look up the individual correctness of individual classifier and select the best one from them. Here it is proposed a classifier local accuracy measurement technique which is used to dynamic classifier selection algorithm. From the result and performance analysis it can be said that numerous classifier selection schemes give better performance than single classifier and dynamic classifier selection algorithm using proposed classifier local accuracy measurement technique gives stable high and better performance.