Detection of Age-Induced Makeup Attacks on Face Recognition Systems Using Multi-Layer Deep Features

Abstract

Makeup is a simple and easy instrument that can alter the appearance of a person’s face, and hence, create a presentation attack on face recognition (FR) systems. These attacks, especially the ones mimicking ageing, are difficult to detect due to their close resemblance with genuine (non-makeup) appearances. Makeups can also degrade the performance of recognition systems and of various algorithms that use human face as an input. The detection of facial makeups is an effective prohibitory measure to minimize these problems. This work proposes a deep learning-based presentation attack detection (PAD) method to identify facial makeups. We propose the use of a convolutional neural network (CNN) to extract features that can distinguish between presentations with age-induced facial makeups (attacks), and those without makeup (bona-fide). These feature descriptors, based on shape and texture cues, are constructed from multiple intermediate layers of a CNN. We introduce a new dataset AIM (Age Induced Makeups) consisting of 200+ video presentations of old-age makeups and bona-fide, each. Our experiments indicate makeups in AIM result in 14% decrease in the median matching scores of a recent CNN-based FR system. We demonstrate accuracy of the proposed PAD method where 93% presentations in the AIM dataset are correctly classified. In additional testing, it also outperforms existing methods of detection of generic makeups. A simple score-level fusion, performed on the classification scores of shape- and texture-based features, can further improve the accuracy of the proposed makeup detector