Transfer Learning for Recognizing Face in Disguise

Face recognition is a method in Machine Learning to recognize objects in the picture or video. Humans have a memory to recognize other people and recognize some objects like animals, plants, living objects, and non-living objects. However, how the computer does that although it has memory? Machine Learning is the technique or method in Computer Vision that can be used, so computers can understand one person's face to another person contained in the image or video. In this paper, the author proposes about testing some popular Convolutional Neural Network (CNN) Model Architecture to see which one is better to recognize the person face dataset in disguised. The author uses the 'Recognizing Disguised Faces' dataset to distinguish 75 classes of faces, and then try to train and test how accurate it can be recognized by the machine, where it will be useful to anyone who needs to explore and develop an Architecture of Deep Learning. This paper is expected to contribute to the field Machine Learning related algorithm that is used to solve the problem in image classification. The experimental results show significant improvement using transfer learning in VGG Models. We then conclude that ImageNet weight best used for face-recognizing using VGG Models.