Deep face recognition using imperfect facial data
Ali Ahmed Elmahmudi, Hassan Ugail · Future Generation Computer Systems · 2019
h i g h l i g h t s• We show the performance of machine learning for face recognition using partial faces and other manipulations of the face such as rotation and zooming which we use as training and recognition cues.• We use the state of the art convolutional neural network based architecture along with the pre-trained VGG-Face model through which we extract features for machine learning.• Our results show that individual parts of the face such as the eyes, nose and the cheeks have low recognition rates though the rate of recognition quickly goes up when individual parts of the face in combined form are presented as probes.