# A Comprehensive Review of Face Morph Generation and Detection of Fraudulent Identities
## 2 Materials and Methods
### Methods of Morph Attack Detection
#### 2.4.2 Feature Extraction and Comparison
Overall, the robustness of the morphing attack detection model was increased in different scenarios, but a significant effect on accuracy was not observed. This study increased robustness against partial morphs from 20% to 87%. In the domain of feature extraction, statistically independent filters were used to extract micro textures from facial morphed images. The filters were trained on natural images. The micro texture disparity was acquired using a binary statistical image feature (BSIF), and a support vector machine (SVM) based classifier classified the morphed images. Experimentation was performed on 450 morphed images, and these morphed images were created using 110 different subjects [33] (International Organization for Standardization, 2009). Feature extraction was done using a BSIF filter. The extracted features were forwarded to the SVM classifier. For this study, the trained BSIF filters were acquired from [19], and these filters were trained on 50,000 image patches [34]. Unsupervised learning was used for training the BSIF filters by utilizing the independent component analysis method [35]. Figure 4: Basic methodology of a differential morph attack detection model. The SVM classifier was trained on a set of normal face and morphed face images. VeriLook face recognition SDK (2015) was used for evaluating its performance against morphed images. According to the FRONTEX guideline rules, the false acceptance rate (FAR) was set to 0.1% (ABC, 2015). The best accuracy results (ACER--average classification error rate) were achieved in this study at 1.73%. Therefore, it is a highly suitable proposed scheme for deployment in facial recognition systems for the detection of morphed images. In a study [27], a pre-trained convolutional neural network (CNN) was utilized for detecting morphed images even if the images were submitted digitally or in the form of a physical document. Alexnet and VGG19 were used in this study. These networks were already trained on the ImageNet database. Both networks were fine-tuned on the created database [36]. This database was created from the database in [23]. Face detection was done from the input image using the Viola-Jones technique [37]. Normalized images were fed to the neural networks of VGG19 and Alexnet in parallel. Extraction of features from the input images was done by the neural networks. Features were extracted from the FC-6 layer of the network, and then these features were converted into a vector and forwarded to the classification section. Fusion (combination) of extracted features from VGG19 and Alexnet was performed, and classification of features was done using P-CRC (probabilistic collaborative representation classifier). This classifier utilized regularized least square regression (LSR) on the learned feature vector in comparison to the test feature vector [38]. The acquired distance was utilized as a morphing score to assess the performance of the morph detector. The results of the model were significantly better than previously done work on the BSIF model. DEER (detection equal error rate) of the model was 15.5% as compared to 26.70% in the case of a print-scan test set, while there was still some difference in the case of a digital test set. A different method was also implemented [24] that extracted features from the morph and original images in the form of residual noise. A deep multi-scale context aggregation network was implemented. This network converted the input images into denoised images, and these denoised images revealed whether the input image was a morph or a genuine facial image. The model performed denoising operations (wavelet denoise [39], block matching and 3D filtering [40], multiresolution bilateral filtering [41], and denoising convolutional neural network [42; 43; 44]) on the input images.