Performance Comparation of Real Time Image Processing Face Recognition for Security System

This research had been developed a system mainly consists of Arduino microcontoller based hardware and neural network based algorithms. The system has been fully assembled and successfully tested. By using two different methods the point feature detector (PFD) method was used as the first method. An Eigen Feature function was utilized to detect feature point of image. The second method is convolutional neural network (CNN) to recognize human face. Using PFD method, a classification value has been setup <11. The classification value is used as classification category of the program to recognize the subject (face image) correctly. By using PFD method, the response of the system from starting of a face image recognition until opening the locker is 20 second. The CNN method used alexnet to classify the image. At least around 300 training input data are use per person. The face recognition’s experiment reached a high recognition’s accuracy of 99.99% level and an average response time of 10 seconds. This research presents how the human face can be recognized and used to control the opening of a door lock.