Marks Fusion: Development Of Facial Marks Detection System And Fusion With Face Recognition System

Facial marks like freckles, moles, scars, pockmarks have been used in the past to identify individuals. There have been developed systems integrating both Facial Marks de- tection with Facial Recognition [17] [2], which showed im- proved performance over only using Facial Recognition. These systems used classic blob detection approaches like LoG (Laplacian of Gaussian) or Fast Radial Symmetry Transform for detecting facial marks, which gave a lot of False Positives, or had people manually annotate fa- cial marks, which is too time consuming. Although there have been significant improvements in detecting Facial Marks using a Convolutional Neural Network, a system integrating this new approach with facial detection has not been implemented yet. This paper improves the state- of-the art in Facial Marks detection by using CNNs with deeper architectures and shows that a system combining a state-of-the-art algorithm in Facial Recognition with a Fa- cial Marks Systems outperforms one that only uses Facial Recognition, especially in the case of monozygotic twins.