## 4 Implementation and Result
### System Operation Verification  
To verify the operation of the system, a victim verification procedure was simulated. Images corresponding to data from PM and AM forms for 25 potential victims were uploaded to the database. Uploading each image triggers a face extraction module and a module that generates a vector representation of the image--embedding--which is automatically stored in the database. Once enough data are collected, the embedding comparison module is activated, matching the most similar images from the AM database with those from the PM database. Based on this, a set of 3 AM form identifiers is generated for each PM form (each victim)--an identification hypothesis--for further verification by operators. The system operation was evaluated in terms of its performance measured using classification metrics, assuming that the problem of victim identity verification was treated as a multi-class classification problem. The following metrics were used for the evaluation:  
* _accuracy_--express the ratio of correctly classified elements to all elements classified: \[accuracy=\frac{number\ of\ correct\ predictions}{total\ number\ of\ all\ predictions}=\frac{TP+TN}{TP+TN+FP+FN}\] (7) _TP_--the number of all correctly classified positive predictions, _FP_--the number of all incorrectly classified positive predictions, _TN_--the number of all correctly classified negative predictions, _FN_--the number of all incorrectly classified negative predictions. * _precision_--expresses the ratio of correctly classified examples from the positive class to all examples that were classified as positive: \[precision=TP/(TP+FP)\] (8) _recall_--the indicator, also referred to as accuracy for a positive class, shows how often a positive class has been classified as a positive class: \[recall=\frac{TP}{TP+FN}\] (9)
* \(F1\) score--the harmonic mean of precision and recall: \[F1=2.\frac{prec\text{-}recall}{prec+recall}\] (10)
* confusion matrix--this is a table visualizing the operation of the algorithm, each row of the matrix represents instances of a real class, while each column represents predictions, which allows quick verification of whether a given class is confused with another class, and if so, with which one. The precision, recall and F1 metrics were determined for each class separately, and then the mean value was determined. Below there are values of selected metrics for the system using the basic model and the finetuned model--Table 3 and Figure 17. Confusion matrix for standard model--1 AM. Figures 17-20 in two variants:  
* assuming that only the AM form indicated first by the system (most similar to the PM form, according to the algorithm) is predictive;
* assuming that the prediction is one of the three AM forms indicated by the system (correct if indicated as one of the three or first if not correctly indicated). Figure 17: Confusion matrix for standard model—1 AM (1 in the matrix means that the identification for the person was correct). Figure 18: Confusion matrix for standard model—3 AM (1 in the matrix means that the identification for the person was correct). Figure 19: Confusion matrix for fine-tuned model—1 AM (1 in the matrix means that the identification for the person was correct). Operation of the system based on a basic embedding generating model (trained on the LFW set only) proved to be very inefficient. The accuracy of person verification reached 8% when indicating only one AM form and 12% when indicating three forms, i.e., three identification hypotheses. Also relevant to the problem being solved is the value of the recall and precision metrics, which in this case correspond in turn to an indication of how often another (incorrect) identity was indicated for a given person and how often other victims have been classified to a given identity. The low performance is due to the fact that the key neural network model is trained on data that are significantly different from the image data used to test the system.