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 problem Which can foundlike error position picture face,
part eye, nose as well as ears that are not completely visible and
also with the addition of accessories such as glasses, beards on
picture face Which influence accuracy introduction face.
Algorithm introduction face use 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 identify facial image recognition correctly. Total time
26.18 seconds of execution required to process the image 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 second.
Based on results processtesting training datasets from each of the
two image classes face Rose And Jiso, as much 170 image face
data training, Andvalidation dataset of 80 facial images. Testing
process andmodel execution time results in the level of accuracy
at epoch to 25 val_accuracy as big as 1.00% And total time
execution epochamounting to 151,587 seconds. Which shows that
the algorithm is deep learning method CNN capable identify
introduction face someone with Good.