Face Recognition Based on PCA and Neural Network

This paper presented a face recognition method based on PCA and Back Propagation Neural Network which is robust and simple algorithm based on image captured by camera. The proposed algorithm is done on three stages, namely face detection, face feature extraction and face recognition. The face detection is performed using Haar-Like Feature. The Haar-Like feature analyzes the pixels in the image into squares by function. The method uses the Adaboost learning algorithm to select a small number of important feature from a large data set. The detected face is then extracted using PCA. PCA will select and reduce the face feature based on eigenvalues of correlation matrix data. We obtain 500 features face extracted using PCA. The face feature becomes the input to neural network for recognition process. The neural network is developed with two hidden layers with 15 nodes on each and three nodes of output layer. The experiment is performed in real time environment. Using 5 faces image data with each data is taken 100 times, the experimental result showed the satisfactory result with 87.5% recognition rate in average.