Classification Of Gender Using Global Level Features In Fingerprint For Malaysian Population

A new approach of algorithm based on the Mark Acree’s theory, focusing on fingerprint global extracted features is proposed and implemented for enhancing gender classification method. This proposed method can automatically execute the ridge calculation process from the 25mm2 fingerprint and enhance the forensic gender classification process. In this study, a relationship between fingerprint global features and a gender of person in Malaysian population is also explored, enhanced and improved by exploiting another five additional fingerprint features. A sample of 3000 fingerprints from 300 respondents of random selection are carefully taken before any relationship can be determined. For the classification part, five extracted features of the fingerprint are used which are Ridge Density (RD), Mean Ridge Count (RC), Ridge Thickness to Valley Thickness Ratio (RTVTR), White Lines Count (WLC) and Mean Pattern Types (PT). Two classification approaches which are the descriptive statistical and data mining are used in order to examine the classification of the gender by using the five extracted features. For data mining classification part, there are four popular machine learning classifiers used which are Bayesian Net.work (Bayes Net.), Multilayer Perceptron Neural Network (MLPNN), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). These four classifiers are used in the data mining task with five test cases each in order to find the accuracies of the gender classification. The accuracy of the results from the proposed method is compared to the Acree Method is shown in terms of relative error. For statistical approach using Ridge Density (RD), the relative error is 3.7% for male respondent and 4.1% for female respondent. Meanwhile, the overall performance of the result from the proposed method achieved more than 90% classification rate for all the classifiers. SVM emerges as the best classifier for all the different cases in order to classify the gender using the results from the proposed method

Multi-Level Pooling Model for Fingerprint-Based Gender Classification

It has been widely reported that CNN (Convolutional Neural Network) has shown satisfactory results in classifying images. The strength of CNN lies in the type and the number of layers that construct it. However, the most apparent drawbacks of CNN are the requirement for a large labeled dataset and its lengthy training time. Although datasets are available, labeling that data is a significant problem. This work mimics the CNN model but only utilizes its pooling layers. The novelty of this model is removing convolution layers and directly processing fingerprint images using pooling layers. Three pooling layer models, namely maximum pooling, average pooling, and minimum pooling, are used to generate fingerprint features to classify their owner gender. These pooling layers are arranged consecutively up to eight levels. Removing convolution layers makes the process straightforward, and the computation is much faster. This study utilized 200 fingerprint datasets from the NIST (National Institute of Standards and Technology), with male and female fingerprints of 100 samples each. The extracted features were then classified using K-NN (K-Nearest Neighbors) algorithm. The proposed method resulted in an accuracy of 61% to 71.5% or an average of 66.25%

Klasifikasi Gender Berdasarkan Fingerprint Menggunakan Metode Naive Bayes Classifier

No two fingerprints are identical, as everyone has their own characteristics. The most fundamental problem lies in the results of the fingerprint image, typically due to inconsistencies in the emphasis of the fingerprint and the position of the fingerprint, resulting in inconsistencies in the thickness of the black line and shifting positions, which negatively impact the overall performance of the system. To solve this issue, research is required on a classifier that assumes all attributes exist independently. The NBC (Naïve Bayes Classifier) is a classifier based on the assumption that all attributes are independent. The NBC method for gender classification based on fingerprints consists of three steps. The initial step is to evaluate the quality of the image to be processed. This is demonstrated by the consistency of the grayscale values, which are not skewed when converted to a binary image. The second is the selection of data that exhibits no data deviation, which also leads to errors in the classification procedure that follows. With the existence of machine learning, class-based measurement formulations can be acquired through training. Even with unbalanced data, it is preferable to use NBC for classification purposes

Multilayer Perceptron Neural Network In Classifying Gender Using Fingerprint Global Level Features

Background/Objective: A new algorithms of gender classification from fingerprint is proposed based on Acree 25mm2 square area. The classification is achieved by extracting the global features from fingerprint images which is Ridge Density, Ridge Thickness to Valley Thickness Ratio (RTVTR) and White Lines Count. The objective of this study to test the effectiveness of the this new algorithm by looking the classification rate. Multilayer Perceptron Neural Network (MLPNN) used as a classifier. Methods: This new algorithm is tested with a database of 3000 fingerprint in which 1430 were male fingerprint and 1570 were female fingerprints. Classification part is tested with different test option. Findings: This study found that women tends to have higher Ridge Density, higher white lines count and higher ridge thickness to valley thickness ratio compared to male same as the previous study. Therefore, we can conclude that this new algorithm is very efficient and effective in classifying gender. Conclusion: The overall classification rate is 97.25% has been achieved

Gender Determination using Fingerprint Features

Several previous studies have investigated the gender difference of the fingerprint features. However, regarding to the statistical significance of such differences, inconsistent results have been obtained. To resolve this problem and to develop a method for gender determination, this work proposes and tests three fingertip features for gender determination. Fingerprints were obtained from 115 normal healthy adults comprised of 57 male and 58 female volunteers. All persons were born in Taiwan and were of Han nationality. The age range was18-35 years. The features of this study are ridge count, ridge density, and finger size, all three of which can easily be determined by counting and calculation. Experimental results show that the tested ridge density features alone are not very effective for gender determination. However, the proposed ridge count and finger size features of left little fingers are useful, achieving a classification accuracy of 75% (P-valu

Biometric presentation attack detection: beyond the visible spectrum

The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and i i) an in-depth analysis of several state-of-theart techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks