# A Comprehensive Review of Face Morph Generation and Detection of Fraudulent Identities
## 3 Results
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Year of Publication** & **Methodology** & **Dataset** & **Results** & **Limitations** \\ \hline
[2] & 2018 & Correlated points are used for the extraction of coconspirator’s face from morphed image. & PMDB and MorphDB (Self-built) & False positive rate of biometric system reduces from 66.4\% to 6.1\%. & Prior knowledge of morphing technique and parameters is required to certify proper extraction of cocomplete’s image. it is also required to manually remove certain traces and artifacts. Moreover only two subjects are used. \\ \hline
[1] & 2019 & Abstraction of accomplice’s image is performed using double network architecture. Moreover, two restoration losses are also used. & FM-database (Self-built) & Accuracy raised 49.82\% to 87.5\% in simple cases. For complex cases like variety of facial expression or occluded faces, accuracy enhanced from 0.4691 to 0.649. & Only Morph-2 images are employed for Limited morphing tools are used for creation of morph images. \\ \hline
[21] & 2020 & Convolutional neural network (CNN) is employed for detection of morph attack & FRAV-ABC & Very high accuracy of 98.7\% is attained for morph attack detection. DEER of 0.78\% to 20.7\% is also reported. & Variation in illumination, facial expressions, and bearings is not created. \\ \hline
[5] & 2020 & Deep learning solution is proposed for morph detection. & MAD database (Self-built) & DEER of 1\% to 7\% is reported & Variety of morphed images is created using four different tools, but those tools are not the state-of-the art tools. Does not deal well with headgear, eyewear, and illumination variation. \\ \hline
[20] & 2020 & Four training methods and layer-wise propagation are used for analyzing the morphed images. & Self-built & 2.8–3.1\% DEER is reported for the method & Data do not includes the images with varying lighting conditions. Moreover headgear, eyewear, and facial hair variations are also not considered. \\ \hline
[46] & 2016 & Binary statistical image features (BSIF) are used for feature representation. Classification is carried out through Support Vector Machine (SVM). & Self-built (ACER) reduced from 3.55\% to 1.73\% & Morphology tools used for creation of morph-2 images were used. \\ \hline
[27] & 2017 & State-of-the-art deep learning models like VGG19 and Aleanet are used for transfer learning. For classification, P-CRC is used. & Self-built & DEER decreases from 26.7\% to 15.0\%. & Data do not includes images with varying lighting conditions. Moreover headgear, eyewear, and facial hair variations are also not considered. \\ \hline
[24] & 2020 & Residual noise is detected using deep architecture. P-CRC classifier is used for the classification of morphed images. & Derivative of PutDB and FRGC & DEER of 2.6-8\% is achieved. Comparison with previous studies is also reported, showing their DEER value between 3.8–42\%. Computational cost also enhanced four times. & Mapping tools used for creation of morph-2 images are employed. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of research work on morph attack detection. It is observed that most of the research work in the literature is tested on self-built data. It led to vague comparisons between different techniques. The morphs generated with such tools leave behind artifacts and residues that can be traced with a visual inspection, so they are very easy to detect by computer systems. Consequently, these systems are hardly ever used by convicts, therefore not portraying real-world circumstances. Techniques assessed on the datasets may produce elevated detection accuracy. However, due to the aforementioned limitations, the real scenarios will not get such outputs. Face morphs created with sophisticated tools with individual attention may lead to misclassification. It is also assumed that most of the detection methodologies work fine with the known data.