## 5 Conclusions  
This paper demonstrates a comprehensive evaluation of fingerprint image gender classification using CNN. The study is divided into three main parts: selection of an optimal CNN for gender classification based on the given datasets, identification of critical fingerprint regions for classification, and exploration of DCAI strategies to enhance classification accuracy. This section delves into the implications of these findings. The results indicate that VGG19 outperforms other architectures in terms of accuracy, precision, recall, and F-score. It achieved a test accuracy of 0.84, highlighting its ability to balance model complexity and generalization. These findings emphasize VGG19's superiority in accurately classifying gender based on fingerprint images. VGG19's successful application is further demonstrated when it is tested across different datasets, including the IsrPoliceDB and three public databases (Table 2). The classification results range from 70% to 95%, showing its ability to generalize across various datasets without overfitting. The results correlate with the size and quality of the images in the dataset. This indicates that VGG19 effectively captures positive samples while minimizing false positives. In order to apply gender classification in a practical manner in a variety of real-world scenarios, this ability to generalize across diverse datasets is vital. The second part of the study focused on identifying and assessing the critical fingerprint region that significantly influence gender classification (Figure 2). It became evident that the outer region holds greater importance in gender classification. This conclusion is in harmony with the existing literature and underpinned by the distinct absence of disruptive elements within the outer region [27]. The methodology employed in this study to define the ROI in fingerprint images yields valuable insights for researchers and practitioners dealing with partial or low-quality fingerprint images. The third part of the study explored DCAI strategies aimed at further enhancing classification accuracy (Figure 3). Among the five DCAI approaches examined, cleanlab-OOD and FLIP-Easy consistently demonstrated improvement in F-scores, with an average increase of 2.5% and 2.75%, respectively. FLIP-Easy appeared to be the optimal approach for several reasons: First, by eliminating 5% of the data with the most inconsistent classifications during training, it ensured that the model focused primarily on data where the classifications were stable; this enhanced the model's generalization. By applying this strategy, overfitting was reduced, model reliability was improved, and independent data could be predicted more accurately. Second, this helped in building a leaner, more resource-efficient model. Data points with fluctuating classifications can slow down the learning process by causing the model to repeatedly adjust its parameters to correct perceived errors; by removing these unstable elements, the model can train faster and use computational resources more efficiently. Lastly, the FLIP-Easy approach encourages a focus on high-quality data. To conclude, FLIP-Easy provides the highest performance for constructing a more generalizable, efficient, and high-performance model. Additional implications for various security and forensic applications--Fingerprints searched against the database do not always yield a match with the perpetrator, especially when the fingerprints are very low quality, and may not be sufficient for direct identification. However, in such cases, gender classification can still play a role in the exclusion or inclusion of suspects and serve as an important investigative lead, focusing the investigator on males or females. Additionally, in cases with limited forensic resources, where not all evidence can be thoroughly analyzed immediately, fingerprints can aid in prioritizing investigative efforts. Therefore, focusing on males or females based on fingerprint classification can significantly streamline the investigative process [27].