## 3 Methods
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Database Name** & **Age** & **Gender** & **Ethnic Group** & **\# of Impressions** & **Image Resolution** & **Type** & **Size** **(Male/Female)** \\ \hline \multirow{2}{*}{IsrPoliceDB} & \multirow{2}{*}{X} & \multirow{2}{*}{V} & \multirow{2}{*}{Israel} & \multirow{2}{*}{1} & JPG & \multirow{2}{*}{Plan} & \multirow{2}{*}{619 (50\%)/652} \\  & & & & & & 688 \(\times\) 975 px & & (50\%) \\ \hline \multirow{2}{*}{NIST-DB4} & \multirow{2}{*}{X} & \multirow{2}{*}{V} & \multirow{2}{*}{Not mentioned} & \multirow{2}{*}{2} & PNG, 96 dpi, & \multirow{2}{*}{Plan (2 impressions of  each finger)} & \multirow{2}{*}{4000} \\  & & & & & & 8 bit, gray & & (2 impressions of  each finger) & (19\%) \\ \hline \multirow{2}{*}{SOCOfing} & \multirow{2}{*}{X} & \multirow{2}{*}{V} & \multirow{2}{*}{African} & \multirow{2}{*}{1} & BMP, 500 dpi, & \multirow{2}{*}{600 subjects \(\times\) 10} & \multirow{2}{*}{4770 (75\%)/1230} \\  & & & & & & 96 \(\times\) 103 px & & (25\%) \\ \hline \multirow{2}{*}{NIST-302} & \multirow{2}{*}{V} & \multirow{2}{*}{V} & \multirow{2}{*}{American} & \multirow{2}{*}{20+} & PNG, 500 dpi, & \multirow{2}{*}{4-finger, 2-thumb, palm} & \multirow{2}{*}{1583 (75\%)/396} \\  & & & & & & & (25\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Fingerprint Databases Utilized in This Study. The databases considered/used in the study include IsrPoliceDB, NIST-DB4, SOCOfing, and NIST-302. The ‘X’ and ‘V’ symbols signify the absence and presence, respectively, of specific characteristics within each database. integrates precision and recall, offering a more robust performance measure for imbalanced datasets. The F-score provides a comprehensive perspective on accuracy, surpassing individual metrics such as precision or recall and enabling clearer understanding and communication of findings, irrespective of dataset class imbalances. Due to the partial or low-quality fingerprint images commonly found at crime scenes, we explored the VGG network's performance in classifying gender based on fingerprints' internal and external cylindrical regions. By examining the specific contributions of the internal and external areas (see Figure 1), we aimed to gain a deeper understanding of the nature of the fingerprint data and its relevance to gender classification tasks in cases of partially or low-quality fingerprints. We evaluated four scenarios: gender classification based on (a) 50% of the inner regions, (b) 60% of the inner regions, (c) 50% of the outer regions, and (d) 40% of the outer regions. The training and testing sets were kept the same in each case. The methodology employed to extract both the inner and outer area of a fingerprint follows a series of steps: Initially, a binary threshold transforms the grayscale fingerprint images, which is succeeded by morphological operations. These operations are designed to reduce noise and isolate the fingerprint. Subsequently, the methodology fits an ellipse around the largest contour, assumed to be the fingerprint. Using these ellipse parameters, the inner and outer regions of the fingerprint are extracted. It is important to note that this methodology is designed to handle fingerprints that are off-center or rotated, as it does not depend on a specific alignment or central positioning within the image. This methodology also assumes that the majority of the original fingerprint is visible within the image. Regarding the DCAI, we employed a comprehensive set of five distinct approaches to identify and rectify issues within the most challenging 5% of data instances. Subsequently, we conducted model retraining using the refined dataset and evaluated the performance of each newly trained model. The five specific DCAI approaches we examined included [18]: Cleanlab Out of Distribution (cleanlab-OOD), FLIP (comprising Easy and Hard variants), and Margin Of Confidence (MOC; Easy and Hard variants). The following sections will provide a detailed explanation of each of these approaches.