Comparative Study of Fingerprint-Based Gender Identification

Abstract

Gender identification is a need in forensics investigation. In addition to their use for identification, fingerprints are used for gender identifications. In this work, we propose a model for fingerprint gender classification focusing on a small dataset with imbalanced classes. First, we applied denoising and equalization filters to the fingerprint images. Then, we cropped the image into a region of interest. After preprocessing the fingerprint image, we extracted fast Fourier transform (FFT) and principal component analysis (PCA) features, and we applied the min-max normalization. We train our models with each of them as well as with their fusion. To balance the initially imbalanced datasets, we used two sampling techniques: random sampling and synthetic minority oversampling Technique (SMOTE). Our comparison of the receiver operating characteristic (ROC) of K-nearest neighbours (KNN), Support Vector Machine (SVM), Decision Tree, and forward neural network showed that the best result for the area under the ROC Curve is given by the SVM classifier. Next, we compared the SVM classifier accuracy for each fingerprint position with FFT features, PCA features, and their fusion. In addition, we applied both random hybrid sampling and SMOTE sampling. We applied our method on the National Institute of Standards and Technology (NIST) special database, 4 gray scale images of fingerprint image. We showed that the fusion of FFT and PCA features gives the best gender identification accuracy when the SMOTE dataset balancing technique is applied. We found that the right ring finger was more appropriate with 75.55% of correct male gender identifications and 91.62% female gender identification. Further investigation into dataset balancing techniques will be applied in the future.

Enhancing Fingerprint Forensics: A Comprehensive Study of Gender Classification Based on Advanced Data-Centric AI Approaches and Multi-Database Analysis

“…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.…”

Sampling Fingerprints From Multimedia Content Resource Clusters

“…The existing literature broadly defines the multimedia fingerprinting concept in the context of audio content and copyright protection [21], [22]. The former is to provide the effective matching of audio clips, and the latter is to preserve the copyright of the multimedia content.…”

Enhancing Fingerprint Forensics: A Comprehensive Study of Gender Classification Based on Advanced Data-Centric AI Approaches and Multi-Database Analysis

“…However, with 65% accuracy in classifying gender, this study demonstrates a moderately favorable outcome Alternative approaches to gender classification based on fingerprint ridges use advanced machine learning and deep learning methods based on the whole fingerprint image. In [7] 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 imbalance.…”