# A Comprehensive Review of Face Morph Generation and Detection of Fraudulent Identities
## 2 Materials and Methods
### Source Databases  
Many datasets are presented in the literature for biometrics and face recognition [11; 12; 13; 14; 15; 16; 17; 18; 19]. For the training and testing of morph attack detection models, the databases shown in Table 1 are used. These databases are used to create morphed images utilized in morph attack detection research studies. It can be seen that different databases have a diverse range of image variations. High image variation is better for creating a generalized morph attack detection model. The number of subjects also facilitates training a generalized and robust model.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Datasets** & **Publication Year** & **No. of Images** & **No. of Subjects** & **Images Per Subject** & **Feature Distinction** & **Research Work Reference** & **Morphing Tools** \\ \hline FRGCv2 & 2005 & 28,021 & 4003 & 7 & Illumination, expression, 3D & [5] & UBO Morpher [2], FaceFusion (2012). & FaceMorpher (2018), OpenCV [18], FaceFusion (2017). & [2] & GIMP Software (2017), Sqirz Morph (2017). & [2] \\ \hline FERET & 1998 & 14,126 & 1199 & Variable & Grayscale and colored lighting a variety of facial expressions and postures, subjects of variant races, facial hair and hair style & [2] & UBO Morpher [2], FaceMorpher (2018), OpenCV [18], FaceFusion (2017). & [2] & GIMP Software (2017), Sqirz Morph (2017). & [2] \\ \hline FM-DB Custom Made & 2019 & 1449 & 63 & 23 & Expression, occlusion, make-up, gender & [1] & Customized morphing process. & [1] & (Script-Code based) \\ \hline AR Face Database [13] & 1998 & 4000 & 126 & Variable illumination, gender & [2] & GIMP Software (2017), Sqirz Morph (2017). & [2] \\ \hline FRAV-ABC & 2020 & 2340 & 1170 & 2 & Age, gender & [21] & GIMP Software (2017), Sqirz Morph (2017). & [2] \\ \hline BU-dDFe [14] & 2008 & 60,600 & 101 & 600 & Expression, race, gender & [20] & Triangle warp, Beier-Neely field morphing method [5], and Fotomorph (2014). & [2] \\ \hline CFD [15] & 2015 & 597 & 597 & 1 & Race, gender & [20] & Triangle warp, Beier-Neely field morphing method [5], and Fotomorph (2014). & [2] \\ \hline FEI (Faculty of Industrial Engineering) [22] & 2015 & 2800 & 200 & 14 & Different poses, facial expression variety, illumination change, eyewear, and varying races & [20] & Triangle warp, Beier-Neely field morphing method [5], and Fotomorph (2014). & [2] \\ \hline PUT [16] & 2008 & 9971 & 100 & Variable & Posture, high resolution & [20] & Triangle warp, Beier-Neely field morphing method [5], and Fotomorph (2014). & [2] \\ \hline SC-Face [17] & 2011 & 4160 & 130 & Variable & Quality, illumination, gender, distance, posture & [20] & Triangle warp, Beier-Neely field morphing method [5], and Fotomorph (2014). & [2] \\ \hline Utrecht ([PERSON], 2008) & 2008 & 131 & 69 & Variable & Expression, gender, race & [20] & Triangle warp, Beier-Neely field morphing method [5], and Fotomorph (2014). & [2] \\ \hline Custom Made Database (All improved) & 2016 & 450 & 110 & Variable & Race, gender & [23] & GIMP Software (2017) \\ \hline Custom Made (morphed included) & 2017 & 783 & 104 & Variable & Race, gender & [22] & GIMP Software (2017) \\ \hline Custom Made (morphed included) & 2020 & 11,293 & 747 & Variable & Illumination, expression & [23] & OpenCV [24] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details about source databases and morph attack detection methodologies.  
Similarly, the total number of images in a respective database also plays a vital role in model training and testing. Different morphing tools are used for the creation of morphed images. The quality of created morphed images varies with the utilized tool. Some tools automatically generate morphed images from original source images through a program script code, while others require manual interference to create morphed images. The studies that have utilized large databases with high variation and a variety of morphing tools depict a more realistic morph attack detection scenario.