Face recognition systems assume that a person's face serves as the unique link to identify them. A morph attack happens when two people with similar facial features morph their faces together, resulting in a face image that can be identified as either of the two contributing individuals. Since the morphed image inherit enough visual traits from both individuals, both humans and automatic algorithms could be deceived by a morphed image. In terms of biometrics, changing one's appearance to impersonate a target identity is a direct attack on the security of face recognition systems. Defending against such attacks necessitates the ability to detect them as distinct identities from their target.
Although they are not always visible in the image domain, many morphing algorithms introduce artifacts in the final image that can be used to detect morph attacks. Since various spectral images allow us to investigate low and high-frequency data separately, we can recognize and isolate these morphing abnormalities in the spatial frequency domain. For this research, we develop a new database that includes the morphed images created using three different techniques and spectral images in different spectral bands. This study studies the potential attack of various efficient face recognition systems from the newly created database using spectral images as a reference set. In addition, this thesis also investigates the human observer's ability to detect the morphed images while examining spectral images. Further, we evaluate the effectiveness of different MAD approaches using spectral bands imaging in order to detect differential morph attacks.