# A Comprehensive Review of Face Morph Generation and Detection of Fraudulent Identities
###### Abstract  
A robust facial recognition system that has soundness and completeness is essential for authorized control access to lawful resources. Due to the availability of modern image manipulation technology, the current facial recognition systems are vulnerable to different biometric attacks. Image morphing attack is one of these attacks. This paper compares and analyzes state-of-the-art morphing attack detection (MAD) methods. The performance of different MAD methods is also compared on a wide range of source image databases. Moreover, it also describes the morph image generation techniques along with the limitations, strengths, and drawbacks of each morphing technique. Results are investigated and compared with in-depth analysis providing insight into the vulnerabilities of existing systems. This paper provides vital information that is essential for building a next generation morph attack detection system. face morphing; biometric forensics; morphed identities; biometrics; face recognition +
Footnote †: journal: Medical  
the contributing faces. Consequently, the morphed image can trick the automated border control system to get illegal authorization. Therefore, the face identification procedure endorses the machine-readable travel document as the captured still of the passenger matches the accomplice's passport. This technique tricks the automated system and does the human inspection. Figure 1 shows the face morphing results of two subjects. The resultant image resembles both the contributing subjects. Moreover, the proportion of the contribution of an image can vary in the morphing process. This results in increasing the detection challenge. Therefore, image morphing can be used by any criminal on the exit control list. That person can use an abettor's morphed image identity and positively obtain travel authorization in an unlicensed state or territory. Various research studies are presented in the literature to reduce this possibility of fraudulent identity. These methods can be grouped based on the methodology acquired. A single-morph attack detection method inspects only the morph image for traces of morph, and the differential morph detection method compares both the query-saved image and the live photo of the traveler [5]. This study focuses on reviewing the latest available morph attack detection techniques. Detailed comparison between different techniques will be done. Source databases will be analyzed and compared with respect to quality, variation, and morphing tools used. This research provides a comprehensive review of existing morph creation and detection techniques. It also analyzes the limitations of the existing work. This work can be used by other researchers to see in-depth progress of the field so far. Presented research analyzes the current and previous work on morph generation, creation, and detection. It also gives direction for future research prospects. The manuscript's significant contributions are:  
1. This research explains and analyzes different morph generation techniques, tools, and their limitations. 2. It presents data repositories used for morph attack detection and their challenges and limitations. 3. It shows the evaluation metrics used as standard practice in the field. 4. It also gives in-depth knowledge and analysis of morph detection techniques and their results on different datasets. Figure 1: Morph-1 (**c**) is a morphed image created from Subject-1 (**a**) and Subject-2 (**b**). Morph-2 (**f**) is a morphed image created from Subject-3 (**d**) and Subject-4 (**e**). (Morphed images are created manually using the tool FotoMorph (2014) and Celebrity Database [6] Adapted from [PERSON] et al. (2015). It also gives the open challenges and future research prospects of the field. The structure of the article is as follows. Section 2 describes the source databases in detail. It also presents the evaluation metrics of the domain. State-of-the-art image morphing and morph attack detection (MAD) methodologies are also described and discussed in Section 2.