# A Comprehensive Review of Face Morph Generation and Detection of Fraudulent Identities
## 4 Conclusions and Future Research Direction  
Several approaches have been proposed previously to tackle the threat of morphing attack detection, and remarkably successful results have been achieved by employing different techniques of image processing and deep learning. Different databases have been explored, and different metrics are used for evaluation of test results. The best morph detection results were acquired in the study using deep face representations with a DEER of 1% to 7% [5]. Similarly, other works also achieved very good results with DEER of 0.78% to 20% [21] and DEER of 2.8% to 3.1% [20]. This review presented the state-of-the-art techniques of the field. A lot of work has been done in the field, but a few challenges need to be addressed in future research. An extensive data repository is very much required that can have a variety of images similar to real life scenarios. Challenging scenarios like facial hair, spectacles, make-up, variation in hair style, facial expression, and posture variation, must be included. Furthermore, age variation in the data is very limited. According to ICAO protocols about travel documents, MRTD is valid for up to ten years. But the dataset has a variance of two years only. So, the experiments must be carried out on a dataset having an age variance of at least ten years. Images with variable attributes associated with subjects' age variant, expression and pose variant, lighting conditions, gender, race, hairstyle, facial hair, head gear, and eyewear should be utilized. Generalized images should be considered to increase the effectiveness of border control checkpoints. Moreover, low quality morphing tools are used in most of the studies for morph data generation. More sophisticated tools should be used for realistic examples.  
This research can also help with hotel check-ins, crime investigations, security agencies, and banks. Furthermore, different countries have different rules for picture size and other image specifications. Image quality may also vary due to the diversity of technology used for image acquisition. Therefore, the morph attack detection method should be capable of dealing with such challenges so that it may be deployed to any location having different acquisition methods, lighting, subjects, and technologies. High-quality professional morphing tools should be used to generate high-quality morphed images in order to simulate a real-world scenario during testing of the model. Therefore, if this proposed model is successfully developed and implemented, then a robust, adaptable, generalized, and accurate model will be available for deployment in any travel or security agency to simplify the process of morph attack detection, and it will certify that duplicitous actions will not go unimpeded.  
It is also observed that 3-morphs are not employed for the morph detection process. The complexity of morph detection will increase if three or more subjects are used for morph creation. This can also lead to a future research track for this problem.  
**Author Contributions:** Conceptualization, S.T.; methodology, S.T., M.H. ([PERSON]) and M.H. ([PERSON]); software, M.H. ([PERSON]), M.A.; validation, S.T. and M.H. ([PERSON]); formal analysis, M.H. ([PERSON]), M.A. and S.T.; investigation, M.H. ([PERSON]) and S.T.; data curation, M.H. ([PERSON]); writing--original draft preparation, M.H. ([PERSON]); writing--review and editing, S.T.; visualization, M.F.A. and M.A.; supervision, S.T., M.H. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable for studies not involving humans or animals.  
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No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.