Performance Analysis Algorithm Deep Learning For Introduction Face

Abstract

Introduction face in world technology own ability Which Enough Good in do his task. introduction face own various problems Which can be found in the error position picture face, part eye, nose as well and ears that are not completely visible, and also with the addition of accessories such as glasses, and beards on the picture face Which influence accuracy introduction face. Algorithm introduction face uses Deep Learning with model network nerve imitation Convolutional Neural Network (CNN). Results from research that done measure analysis algorithm Deep Learning with Convolutional Neural Network method for face recognition use Notation Big-O. With level accuracy predictions model reached 0.99928075 or about 99.93%. model is successful in identifying facial image recognition correctly. A total time of 26.18 seconds of execution is required to process the image and make predictions with the CNN model. Execution complexity time algorithm Big-O Notations (O) in introduction image performed face did not improve significantly with image size (fixed in CNN model), with constant results of CNN model time complexity as constant or O(1) time execution recorded around 26 seconds. Based on the results process-testing training datasets from each of the two image classes face Rose And Jiso, as much as 170 image face data training, And validation dataset of 80 facial images. Testing process and model execution time results in the level of accuracy at the epoch to 25 val_accuracy as big as 1.00% And total time execution epoch amounting to 151,587 seconds. This shows that the algorithm is a deep learning method CNN capable of identifying the introduction face of someone with a Good