## 1 Introduction  
A fingerprint found at a crime scene is important evidence in a criminal investigation. Fingerprints are collected and compared against an existing database of fingerprint images in the police database. Comparisons are made by finding minutiae features [1, 2] and comparing them to those recorded in the police database. If a match is found, the suspect's identity can be established. While minutiae marking conventionally undergoes automated procedures, occasions may arise necessitating manual interventions--specifically involving the removal and modification of minutiae, owing to the presence of artifacts. Beyond the fact that the manual extraction of minutiae features is tedious work and prone to errors, the main disadvantages of this technique are: (1) fingerprints that are not of sufficient value are excluded and (2) attempts are not made to extract additional information beyond comparison with the database--for example, gender, age, and other information [3]. Fingerprints being used as a gender classification method is an exciting research frontier in forensic science. Research on this topic, first pioneered by [PERSON] in 1999 [4], has now spanned over 20 years. The most recent literature review encompassing this work was published in 2021 [5]. The review concluded that fingerprint ridge density could be a reliable parameter for gender classification. It also concluded that the number of ridges present on a fingerprint per unit area varies between individuals and shows differences based on gender. Female fingerprints tend to have a higher ridge density than male's. However, the mean density of fingerprint ridges varies between populations, as demonstrated over different datasets in various studies [6; 7]. For instance, a study published in [6] utilized different image processing techniques, specifically minutiae detection and feature extraction; the study demonstrated 65% accuracy in gender classification, indicating moderate success. This success was further validated by studies conducted in 2023 [7]. Alternative approaches to gender classification based on fingerprint ridges use advanced machine learning and deep learning methods based on the whole fingerprint image. In [8], the authors introduce an approach that leverages Fast Fourier Transform (FFT), Principal Component Analysis (PCA) features, and min-max normalization. The models are trained utilizing a Support Vector Machine (SVM) classifier and incorporate sampling techniques such as SOMAT to address dataset imbalances. In this study, the right ring finger emerged as the most suitable point of interest for gender identification. It achieved an accuracy rate of 75% and 91% for males and females, respectively. The study refrained from employing deep learning methodologies and relied on a single dataset. Another article [9] presented 99% accuracy on the NIST-DB4; however, the article did not provide references or specifics regarding the precision of the train-test division when considering the occurrence of two impressions for each fingerprint and the gender balance, resulting in uncertainty regarding those results. In contrast, when the second impression of each finger was considered [10], the gender classification score was significantly lower--65%. One of the most common publicly available databases for gender classification is SOCOfing. In [11], gender classification performance was evaluated using single finger-based classification, resulting in 77% average accuracy. Remarkably, when employing a weighted approach that considered multiple fingers simultaneously, accuracy significantly improved, achieving a 90% accuracy rate. However, gender classification based on multiple fingerprints is theoretical. According to our knowledge, it has no practical applications in crime scene investigation. This is primarily due to the rarity of encountering more than one fingerprint at a crime scene, as supported by prior research [12]. In this study, the authors employed only an elementary 5-layer CNN [13] architecture.