# A Multicomponent Face Verification and Identification System
## 3 Results  
### Face Identification and Verification Results  
This section presents the results of the experiments conducted to evaluate the performance of the face recognition algorithms on the curated ID vs. Spot dataset. The results are presented in terms of the aforementioned evaluation metrics. In our research, we adopted a data-driven approach to determine the optimal cosine similarity threshold for face verification. This method involved analyzing the distributions of cosine distances for both positive (same person) and negative (different persons) pairings in the dataset. By employing a decision tree algorithm, we identified the threshold that maximized information gain and thereby optimally distinguished between identities. Specifically, a decision stump, i.e., a depth-1 decision tree, was employed, using cosine similarity as the sole input feature. The objective was to identify a threshold that maximally distinguishes between positive (same identity) and negative (different identity) face pairings by maximizing information gain, measured via entropy. To ensure generalizability and avoid overfitting, a five-fold cross-validation strategy was applied, selecting the averaged threshold. Of utmost importance was to reduce the FAR in order to achieve a highly secure system. This threshold, set at 0.235, was crucial in enhancing the precision of the face verification process ensuring high accuracy and reliability. Concerning the face identification task, Table 1 shows the Rank-1 and Rank-5 identification rates attained by each face recognition algorithm and the baseline model on the curated ID vs. Spot dataset utilizing 3880 pairs of images. Figure 2 exhibits the ROC curves of the face recognition algorithms and the baseline model on these image pairs. As shown in Table 1, ArcFace outperformed other models in terms of Rank-1 and Rank-5 identification rates. However, it is pertinent to also consider the inference time, which significantly impacts the real-time applicability of these algorithms. The inference time for ArcFace was measured at 97 ms, whereas FaceNet required only 52 ms. This lower inference time is crucial for real-time face recognition tasks where rapid processing is imperative. Therefore, despite the slightly higher identification rates of ArcFace (and the similar achieved AUC of 0.99), FaceNet was chosen due to its superior speed, demonstrating a more balanced trade-off between accuracy and performance speed, making it more suitable for the proposed system. The MobileNetV2 and Dlib models also demonstrated competitive performance, although they lagged behind in accuracy. For the evaluation of face verification, we meticulously constructed a dataset comprising 3967 pairs of ID and spot RGB photos, each representing either the same individual or different individuals. This balanced dataset allowed for a robust assessment of the algorithms' performance under varying conditions. The same threshold of the cosine similarity, as in face identification, was used for face verification as well. Thus, we rigorously tested each pair against a cosine similarity measure threshold of 0.235, a critical parameter for distinguishing between matches and non-matches, computed as stated earlier. The evaluation process was designed to measure the algorithms' accuracy in correctly identifying true positives (accurate matches) and avoiding false positives (erroneous matches). Notably, FaceNet emerged as a highly effective tool in this evaluation, achieving an outstanding accuracy of 98.6%. This high level of accuracy highlights FaceNet's capability in consistently and reliably verifying facial identities, even in a diverse and challenging dataset. The results from this evaluation serve as a testament to the robustness and reliability of FaceNet.