## 1 Introduction
Moreover, it is noteworthy that the network was trained \"from scratch\", without the use of any beneficial fine-tuning techniques. Another study, conducted on the SOCOfing dataset [14; 15], reported an accuracy rate of 90% on a restricted subset comprising 2000 fingerprints out of the total 6000 fingerprints available in the SOCOfing database. Notably, there is limited clarity regarding the specific criteria employed to select the 2000 fingerprints from the larger pool of 6000. A similar phenome was also reported in [7]. In summary, research articles focusing on the SOCOfing database have reported gender classification outcomes of approximately 75% when employing neural network-based approaches. When multiple fingerprints are used (though not practically applicable), accuracy rates of as high as 90% have been observed. Hence, it is evident that a baseline accuracy of around 75% is commonly encountered in gender classification tasks conducted on the SOCOfing dataset. This is indicated by references such as [16] and others. In the context of studies utilizing advanced techniques on private fingerprint datasets, a recent comprehensive publication [17] conducted an extensive comparative analysis. This analysis encompassed nine widely employed classifiers including CNN, Support Vector Machine (SVM) with three distinct kernels, k-Nearest Neighbors (kNN), Adaboost, J48, ID3, and Linear Discriminant Analysis (LDA). Their methodology involved gender classification based on various fingerprint features, among them ridge-density, ridges, minutiae, and fingertip-size (FTS). The results revealed that CNN achieved the highest success rate for gender classification in this context. The results showed an accuracy of 95%. However, it is important to note that these results were based on private, high-quality fingerprint images collected in controlled laboratory settings (e.g., 500 dpi resolution with each finger scanned three times to ensure image quality), which may not be representative of real-world crime scene scenarios. Moreover, a somewhat redundant methodology that uses CNN for classification was utilized, when it is essentially a method for extracting features. The Data-Centric Artificial Intelligence (DCAI) approach shifts the AI paradigm by placing more emphasis on data rather than algorithms or models [18; 19]. Instead of solely concentrating on the model or algorithm itself, this approach analyzes data to identify which parameter instances have the most significant impact on classification. It then re-trains models based on these influential instances. In recent years, this method has been proven to offer distinct advantages over traditional approaches. DCAI represents an alternative paradigm in artificial intelligence that prioritizes data over algorithms. By implementing a set of margin-based criteria, we effectively filter out uncertain classification, and as mentioned, enable the model to learn from more reliable and informative examples. This approach has demonstrated the efficacy of the margin-of-confidence clean method in improving the performance and generalization capabilities of deep learning models. To conclude, this study's primary contributions encompass several key aspects:  
* Cross-Dataset Gender Classification Evaluation: For the first time, this research compares gender classification performance across multiple datasets, including three publicly available databases and one proprietary internal database. This cross-dataset evaluation is critical for establishing the robustness and generalizability of the proposed CNN methodology, as it mitigates the biases and limitations inherent to single-dataset studies. * Enhanced Analysis of Low-Quality and Partial Fingerprints: Recognizing the practical challenges in fingerprint investigations, this study specifically targets the classification of gender from fingerprints that are partial or of low quality.