Identical Twins Face Morph Database Generation

Kelsey O'Haire, Sobhan Soleymani, Baaria Chaudhary, Jeremy M. Dawson, Nasser M. Nasrabadi

Abstract: By combining two or more face images of look-alikes, morphed face images are generated to fool Facial Recognition Systems (FRS) into falsely accepting multiple people, leading to failures in security systems. Despite several attempts in the literature, finding pairs of bona fide faces to generate the morphed images is still a challenging problem. In this paper, we morph identical twin pairs to generate extremely difficult morphs for FRS. We first explore three methods of morphed face generation, GAN-based, landmark-based, and a wavelet-based morphing approach. We leverage these methods to generate morphs from the identical twin pairs that retain high similarity to both subjects while resulting in minimal artifacts in the visual domain. To further improve the difficulty of recognizing morphed face images, we perform an ablation study to apply adversarial perturbation to the morphs such that they cannot be detected by trained morph classifiers. The evaluation of the generated identical twin morphed dataset is performed in terms of vulnerability analysis and presentation attack error rates.