Gender Classification from Fingerprint Using Hybrid CNN-SVM

Gender classification is used in numerous applications such as biometrics,criminology,surveillance,HCI,and business profiling.Although biometric factors like gait,face,hand shape,and iris have been used to classify people into genders,the majority of research has focused on facial traits due to their more recognizable qualities.This research employs fingerprints to classify gender,with the intention of being relevant for future studies.Several methods for gender classification utilizing fingerprints have been presented in the literature,including ANN,KNN,Naive Bayes,the Gaussian mixture model,and deep learning-based classifiers.Although these classifiers have shown good classification accuracy,gender classification remains an unexplored field of study that necessitates the development of new approaches to enhance recognition accuracy,computation,and running time.In this paper,a CNN-SVM hybrid framework for gender classification from fingerprints is proposed,where preprocessing,feature extraction,and classification are the three main components.The main goal of this study is to use CNN to extract fingerprint information.These features are then sent to an SVM classifier to determine gender.The hybrid model’s performance measures are examined and compared to those of the conventional CNN model.Using a CNN-SVM hybrid model,the accuracy of gender classification based on fingerprints was 99.25%.

Non-destructive silkworm pupa gender classification with X-ray images using ensemble learning

Sericulture is the process of cultivating silkworms for the production of silk.High-quality production of silk without mixing with low quality is a great challenge faced in the silk production centers.One of the possibilities to overcome this issue is by separating male and female cocoons before extracting silk fibers from the cocoons as male cocoon silk fibers are finer than females.This study proposes a method for the classification of male and female cocoons with the help of X-ray images without destructing the cocoon.The study used popular single hybrid varieties FC1 and FC2 mulberry silkworm cocoons.The shape features of the pupa are considered for the classification process and were obtained without cutting the cocoon.A novel point interpolation method is used for the computation of the width and height of the cocoon.Different dimensionality reduction methods are employed to enhance the performance of the model.The preprocessed features are fed to the powerful ensemble learning method AdaBoost and used logistic regression as the base learner.This model attained a mean accuracy of 96.3%for FC1 and FC2 in cross-validation and 95.3%in FC1 and 95.1%in FC2 for external validation.

A 3D morphometric perspective for facial gender analysis and classification using geodesic path curvature features

The relationship between the shape and gender of a face, with particular application to automatic gender classification, has been the subject of significant research in recent years. Determining the gender of a face, especially when dealing with unseen examples, presents a major challenge. This is especially true for certain age groups, such as teenagers, due to their rapid development at this phase of life. This study proposes a new set of facial morphological descriptors,based on 3D geodesic path curvatures, and uses them for gender analysis. Their goal is to discern key facial areas related to gender, specifically suited to the task of gender classification. These new curvature-based features are extracted along the geodesic path between two biological landmarks located in key facial areas.Classification performance based on the new features is compared with that achieved using the Euclidean and geodesic distance measures traditionally used in gender analysis and classification. Five different experiments were conducted on a large teenage face database(4745 faces from the Avon Longitudinal Study of Parents and Children) to investigate and justify the use of the proposed curvature features. Our experiments show that the combination of the new features with geodesic distances provides a classification accuracy of 89%. They also show that nose-related traits provide the most discriminative facial feature for gender classification, with the most discriminative features lying along the 3D face profile curve.