Disguised Face Recognition using Deep Learning

Abstract

Abstract: The accuracy of facial recognition has significantly improved thanks to deep learning-based methods. However, several factors including facial ageing, disguises, and position fluctuations impair the efficiency of automatic face recognition algorithms. Disguises are commonly utilized to conceal one's identity or assume the identity of someone else by intentionally or unintentionally altering one's facial features. Here, we intend to make use of the Disguised Faces in the Wild (DFW) small-scale training data. Deep Convolutional Neural Networks(DCNNs) will be trained for general face recognition. The IIIIT-D testing data set will be used for model evaluation because it exhibits greater performance. The IIIT-D testing dataset is used to gauge the effectiveness of the model's performance when applied to faces that have been deliberately disguised. The performance of the results is encouraging and suggests that DCNNs have a chance of successfully recognizing disguised faces.