FACE CLASSIFICATION OF IN 360 DEGREE IMAGES (FISH EYE) USING TENSORFLOW
Abstract
Technology knows no boundaries, in fact it always shows new developments, one of which is classification in pictures. Human face classification is a method used to distinguish the characteristics of a person's facial pattern. The face classification system is an application that can find out a person's face according to the human face image that has been trained and stored in the machine's database. It is hoped that this application system can work well for classifying human faces in 360˚ image formats that have significant distortion
Classification of human faces can be done in various ways, one of which is the Convolutional Neural Network (CNN) method using Tensorflow. This final project is carried out using 5 classifications of human face datasets totaling 6600 images that have been trained with an image size of 180 x 180 using a 360˚ camera and the Python programming language.
The classification of human faces in 360˚ (fish eye) images was successfully carried out with a percentage of 65% true detection and 35% false detection from the total 135 images that have been tested. In further research, other deep learning methods can be used to obtain better classification accuracy
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