# Comparative Study of Fingerprint-Based Gender Identification

Authors: Lamia Berriche
Journal: Security and Communication Networks
 (2022)

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## 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) ofK-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.


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## Body

## 1. Introduction

Biometrics is the science of recognizing individuals based on their physical, behavioural, and physiological properties, such as fingerprints, face, iris, gait, and voice [1, 2]. Biometrics could be used not only in recognition systems but also for deducing individual attributes such as age, gender, and ethnicity [3]. Using biometric information in such a context is referred to as soft biometrics or light biometrics. Soft biometrics has a variety of applications such as gender or age-specific access control, video retrieval, offering customized gender or age services, search space reduction in large biometric databases such as in both cloud and noncloud based authentication systems [4–6], and forensics use [1–3, 7, 8].There are many methods to determine gender using biometrics such as iris, gait, hand, voice, and fingerprint [7]. Both minutiae and texture features were used to deduce gender identity from a fingerprint [9–11]. High-quality images are required to extract minutiae features, such as ridge count, valley count, and ridge valley density. On the other side, texture features could be extracted using some transforms, such as discrete wavelet transform (DWT) [12] and fast Fourier transform (FFT) techniques [13]. DWT is frequently used in fusion with singular value decomposition (SVD) [14] and with principal component analysis (PCA) [15]. Transform feature extraction is used for fingerprint matching [13, 16], but it has not yet been investigated for gender identification.In this work, we compare FFT, PCA, and fused features for gender identification. We also compare the performance ofK-nearest neighbours (KNN), support vector machine (SVM), decision tree, and artificial neural networks for gender classification. We focused on one challenging issue in the classification problem, which is the small sized dataset with imbalanced classes. In addition, we investigate the accuracy of our model for different fingers.In Section2, we will present the materials and methods, where we start by presenting the state-of the art methods, and then we show our method. In Section 3, we present our results with a discussion. In Section 4, we conclude our paper with a summary of our results and future work.


## 2. Materials and Methods

### 2.1. Literature Review

Gnanasivam and Muttan [17] proposed a system that determines the gender of a person based on a fingerprint using 6-level DWT and SVD, with an internal database containing 3570 fingerprints. For classification, they used the KNN classifier to compare the feature vector of the input fingerprint with the feature vector in the database. The average accuracy result for a 6-level DWT is 89.32% for males and 81.59% for females. The best finger to identify females was the left little finger and the right thumb for males. On the other hand, the average accuracy using SVD as a feature extraction algorithm is 91.74% for males with the right index giving the best result and 83.30% for females with the left little finger with the best result. The combined 6-level DWT and SVD give an average accuracy rate of 91.67% for males and 84.89% for females. Overall, the success rate declines from the little finger to the thumb.In [18], the authors proposed a system for gender classification based on fingerprints using the Haar wavelet in the frequency domain and Canny edge detection. The proposed system is done on an internal database consisting of 275 fingerprints for males and 275 fingerprints for females. For gender classification, they used the KNN classifier. The result of the proposed system is 98%.Tarare et al. [12] proposed a fingerprint-based gender classification system using the DWT transform on an internal dataset consisting of 100 fingerprints for males and 100 fingerprints for females. A 6-level DWT transform was used to extract the features, followed by a KNN classifier. Their accuracy was about 52% for males and 72% for females.In [19], the authors proposed a system that determines the gender based on fingerprints using DWT and SVD, on a dataset of 1000 fingerprints, 500 for males and 500 for females. The success rate of gender classification with a DWT of level 6 is 82.90% for males and 82.60% for females.Abdullah et al. [20] developed a new algorithm for calculating the fingerprint global features to detect gender. The fingerprint images were collected using a Fuji Xerox scanner. The algorithm is based on the calculation of ridge count by counting the connected black and white pixels in the image. Then they compute the ridge density. The success rate of the proposed method is about 74.5%.In [21], a DWT and artificial neural network (ANN), fingerprint-based gender identification system is proposed. After cropping, resizing, and thresholding the fingerprint image, feature extraction was carried out using discrete wavelet transform (DWT) at 6 levels of decomposition. The extracted features were used for training an ANN with the back propagation algorithm. Fingerprint images were obtained from publicly available online datasets. They used a dataset of 300 images of left thumbprints where 200 images were used for training and 100 images for testing. They achieved 78% accuracy for males and 82% for females.Iloanusi and Ejiogu proposed convolutional neural network (CNN) architecture for classifying gender from the fingerprints of each of the five fingers, in [22]. They used two datasets: their dataset with 8025 images from 239 different subjects and the Sokoto Coventry fingerprint dataset. They applied image processing-based data augmentation. They showed that certain fingers give a better classification with a certain gender than the others. They reached an average validation accuracy of 80% for the thumb, finger for their dataset and 83% for the Sokoto dataset. They proved that the performance can be improved by classifying gender from the fingerprints of fused combinations between the five right-hand fingers. They found that the best enhancements for female gender identification were found for the mixture thumb-index-pinkie, which gives an accuracy of 94.7%. The same average accuracy was reached for male gender identification with the combination thumb-middle-ring. The same fusion also provides the best average accuracy of 91% with their dataset and with the Sokoto dataset, which is 84%.In [23], the authors used CNN with both ReLu and Tanh activation functions. They trained their convolutional models on the National Institute of Standards and Technology (NIST) DB4 dataset. They reached an accuracy of 99% with ReLu activation and 20 epochs.In [24], authors proposed a comparative study between AdaBoost, SVM with linear radial basis function (RBF) and polynomial kernel, KNN, J48, the Iterative Dichotomiser (ID3), linear discriminant analysis (LDA), and a CNN. They used six feature extraction methods: FFT, DWT, SVD, ResNet, VGG, and DDC-ResNet. Their experiments were conducted on a dataset of 6000 fingers. They showed that SVM with polynomial kernel and CNN give the best accuracy results. Their SVM classifier reached 97.43% for male gender classification, and 93.24% for female identification. Their CNN classifier reached 97.52% and 95.48% for male and female gender classification, respectively. Then they used CNN with DDC-ResNet to identify the best finger for gender discrimination. They showed that the right ring finger gives an average accuracy of 92.455%.


### 2.2. Methodology

We propose a gender identification system that consists of three main phases: preprocessing, feature extraction with normalization, data balancing, and classification. We applied both PCA and FFT for feature extraction. We compared four types of classifiers:K-nearest neighbour, support vector machine, decision tree, and forward neural networks. Then, we studied the effects of feature fusion and different data balancing techniques on the best classifier. Figure 1 shows our system architecture.Figure 1
System architecture.


#### 2.2.1. Preprocessing

The preprocessing stage aims to set up the images for the subsequent stages. As the images are captured from the camera or scanner, we start by applying noise removal filters, then applying equalization to enhance the contrast of the image. Finally, we select a region of interest from each image to be a candidate for feature extraction.(1) Denoising and Equalization. Image noise is a random distribution of brightness or color information in the image produced when the image is captured by a camera or scanner. Fingerprint images undergo a variety of noises such as random impulsive noise, salt and pepper noise, Gaussian noise, Poisson noise, and so on [25]. After applying a median filter for denoising, we applied a histogram equalizer to enhance image contrast [26]. Original and preprocessed images are shown in Figure 2.Figure 2
Original and preprocessed finger image. (a) Original image and (b) denoised and equalized image.
(a)(b)(2) Region of Interest. The ROI is the more significant region of the fingerprint. Extraction of the ROI begins with detecting the core point, which starts by estimating the orientation field. To detect the orientation, there are many methods. We use the gradient-based method that was proposed in [27]. The algorithm starts by dividing the fingerprint image into nonoverlapping blocks of size w × w. Then, it calculates the gradients gx (x, y) and gy (x, y) for each pixel (x, y) with respect to x and y axis, followed by the calculation of the local orientation. After the orientation field detection, the core point is detected; for any fingerprint, the curvature above the core point is horizontal but is vertical under the core point. A region of size 128 × 128 around the core point is selected, see Figure 3.Figure 3
Finger ROI.(3) Feature Extraction Stage. In this stage, features are extracted from the region of interest. The ROI of the fingerprint can be passed into two domains of feature extraction, either frequency domain or spatial domain or both. We used both FFT and PCA for feature extraction. Then, we applied min-max feature normalization.(4) Fast Fourier Transform. Fourier transform converts the spatial domain input into the frequency domain. We implemented it using the 2-D fast Fourier transform algorithm [8].(1)Fx,y=∑i=0N−1∑j=0N−1fi,je−i2πxiN+yjN,where f(i, j) is an image pixel value.We first applied the FFT to the whole ROI image. Afterward, we divided the power spectrum image into 4 blocks [28]. Because energy distribution is greater at the four corners of the transformed image compared to the middle region, we used further decomposition towards the corners (see Figure 4). Altogether, we divided each block into 10 subblocks, as shown in Figure 5.Figure 4
Fingerprint image power spectrum.Figure 5
Fingerprint image power spectrum subblocks.Afterward, we calculated the mean (2) standard deviation (3) and entropy (4) of each decomposed subblock of size LbX Lb.(2)Meanb=1LbLb∑i=0Lb∑i=0LbFi,j,(3)STDb=1LbLb∑i=0Lb∑i=0LbFi,j−Meanb2,(4)Hb=−∑​pilogpi,where Fi,j is the value of the FFT transform at pixel i,j and pi is the probability of a gray-level i obtained from the normalized histogram of the power spectrum image.Each subimage gives ten nonoverlapping blocks after 4 levels decomposition that yields to a feature vector of size 30 per subblock. So, a feature vector of size 120 is formed for each fingerprint image.(5) Principle Component Analysis. PCA is a statistical technique used in the reduction of data dimensions based on mathematical formulation [14]. First, the ROI images are converted into vectors and stacked in a matrix, see equation (5). Afterward, we applied the eigenvalue decomposition to the covariance matrix of the ROI images (equations (6)–(8)), ordered the eigenvalues in their descending order, and selected the greatest ones with an 80% variance ratio. In Figure 6, shows the decrease of the feature energy. We notice that approximately 160 out of 200 features hold the image energy.Figure 6
Features energy decrease.Finally, each ROI is projected on the more representative eigenvectors corresponding to the selected eigenvalues.(5)ROI=ROI1ROI2…ROIK,(6)M=1NNK∑i=0N−1N−1∑j=0K−1ROIi,j,(7)C=ROI−MROI−MT,(8)C=VΛVT.


#### 2.2.2. Min-Max Normalization

To reduce the variability between feature values, we applied normalization. We selected the min-max normalization for our model for each feature value we applied(9)featuren=featuren−minfeaturesmaxfeatures−minfeatures,where minfeatures and maxfeatures are, respectively, the minimal and the maximal values over a feature vector.


#### 2.2.3. Classification Stage

The final stage is the classification stage. We used four classifiers: support vector machine (SVM), K-nearest neighbor (KNN), decision tree, and artificial neural network (ANN).The purpose of KNN is to calculate the minimum distance between feature vectors, by computing the distance between the test data and the training data [20]. The decision is made based on the computation of the distance to k neighbours. In our model, we decide for k = 3.SVM creates a hyperplane that separates the two different classes. The SVM classifier exists with different kernel types: linear, RBF, and polynomial. Kernel-type and other SVM parameters can be optimized with hyperparameters optimization techniques [29].A decision tree aims to create a model that is sketchily represented as a tree where each node represents a decision conditions based on the data features.Neural networks are inspired by the learning process occurring in human brains. They consist of an artificial network of functions, called parameters and neurons, which examines the data and passes it from layer to layer. The classification result is given by the last layer neurons’ output. In our work, we established a feedforward neural network model with 20 hidden layers, with scaled conjugate gradient as the training function and the cross entropy for the performance measure during training.Classifier performance is affected by the dataset properties such as dataset size and class imbalance, where the number of observations per class is not balanced. The dataset that we have chosen contains more male fingerprints than female, see Table1. We solved the imbalanced dataset issue by using:(i)
Random undersampling of the majority class: In this sampling technique, we randomly select observations from the majority class.(ii)
Hybrid random oversampling of the minority class and undersampling of the majority class: In this technique, we randomly select observations from the majority class and duplicate observations from the minority class.(iii)
Generation of synthetic minority class data points [30]: synthetic observation of the minority class is produced by synthesizing new samples from the original ones using SMOTE technique.Table 1
Number of female and male images in NIST fingerprint datasets.
FingersFemalesMales(1) Right thumb56338(2) Right index56278(3) Right middle62258(4) Right ring64264(5) Right little56256(6) Left thumb55275(7) Left index55235(8) Left middle69251(9) Left ring66266(10) Left little58244The performance of the classifiers is measured using the (receiver operating characteristic) ROC curve and the accuracy. The ROC curve shows the trade-off between sensitivity and specificity.(10)Accuracy=TP+TNTP+FP+TN+FN,Sensitivity=TPTP+FN,Specificity=TNTN+FP,where TP represents true positive, TN represents true negative, FP represents false positive, and FN represents false negative.


## 2.1. Literature Review

Gnanasivam and Muttan [17] proposed a system that determines the gender of a person based on a fingerprint using 6-level DWT and SVD, with an internal database containing 3570 fingerprints. For classification, they used the KNN classifier to compare the feature vector of the input fingerprint with the feature vector in the database. The average accuracy result for a 6-level DWT is 89.32% for males and 81.59% for females. The best finger to identify females was the left little finger and the right thumb for males. On the other hand, the average accuracy using SVD as a feature extraction algorithm is 91.74% for males with the right index giving the best result and 83.30% for females with the left little finger with the best result. The combined 6-level DWT and SVD give an average accuracy rate of 91.67% for males and 84.89% for females. Overall, the success rate declines from the little finger to the thumb.In [18], the authors proposed a system for gender classification based on fingerprints using the Haar wavelet in the frequency domain and Canny edge detection. The proposed system is done on an internal database consisting of 275 fingerprints for males and 275 fingerprints for females. For gender classification, they used the KNN classifier. The result of the proposed system is 98%.Tarare et al. [12] proposed a fingerprint-based gender classification system using the DWT transform on an internal dataset consisting of 100 fingerprints for males and 100 fingerprints for females. A 6-level DWT transform was used to extract the features, followed by a KNN classifier. Their accuracy was about 52% for males and 72% for females.In [19], the authors proposed a system that determines the gender based on fingerprints using DWT and SVD, on a dataset of 1000 fingerprints, 500 for males and 500 for females. The success rate of gender classification with a DWT of level 6 is 82.90% for males and 82.60% for females.Abdullah et al. [20] developed a new algorithm for calculating the fingerprint global features to detect gender. The fingerprint images were collected using a Fuji Xerox scanner. The algorithm is based on the calculation of ridge count by counting the connected black and white pixels in the image. Then they compute the ridge density. The success rate of the proposed method is about 74.5%.In [21], a DWT and artificial neural network (ANN), fingerprint-based gender identification system is proposed. After cropping, resizing, and thresholding the fingerprint image, feature extraction was carried out using discrete wavelet transform (DWT) at 6 levels of decomposition. The extracted features were used for training an ANN with the back propagation algorithm. Fingerprint images were obtained from publicly available online datasets. They used a dataset of 300 images of left thumbprints where 200 images were used for training and 100 images for testing. They achieved 78% accuracy for males and 82% for females.Iloanusi and Ejiogu proposed convolutional neural network (CNN) architecture for classifying gender from the fingerprints of each of the five fingers, in [22]. They used two datasets: their dataset with 8025 images from 239 different subjects and the Sokoto Coventry fingerprint dataset. They applied image processing-based data augmentation. They showed that certain fingers give a better classification with a certain gender than the others. They reached an average validation accuracy of 80% for the thumb, finger for their dataset and 83% for the Sokoto dataset. They proved that the performance can be improved by classifying gender from the fingerprints of fused combinations between the five right-hand fingers. They found that the best enhancements for female gender identification were found for the mixture thumb-index-pinkie, which gives an accuracy of 94.7%. The same average accuracy was reached for male gender identification with the combination thumb-middle-ring. The same fusion also provides the best average accuracy of 91% with their dataset and with the Sokoto dataset, which is 84%.In [23], the authors used CNN with both ReLu and Tanh activation functions. They trained their convolutional models on the National Institute of Standards and Technology (NIST) DB4 dataset. They reached an accuracy of 99% with ReLu activation and 20 epochs.In [24], authors proposed a comparative study between AdaBoost, SVM with linear radial basis function (RBF) and polynomial kernel, KNN, J48, the Iterative Dichotomiser (ID3), linear discriminant analysis (LDA), and a CNN. They used six feature extraction methods: FFT, DWT, SVD, ResNet, VGG, and DDC-ResNet. Their experiments were conducted on a dataset of 6000 fingers. They showed that SVM with polynomial kernel and CNN give the best accuracy results. Their SVM classifier reached 97.43% for male gender classification, and 93.24% for female identification. Their CNN classifier reached 97.52% and 95.48% for male and female gender classification, respectively. Then they used CNN with DDC-ResNet to identify the best finger for gender discrimination. They showed that the right ring finger gives an average accuracy of 92.455%.


## 2.2. Methodology

We propose a gender identification system that consists of three main phases: preprocessing, feature extraction with normalization, data balancing, and classification. We applied both PCA and FFT for feature extraction. We compared four types of classifiers:K-nearest neighbour, support vector machine, decision tree, and forward neural networks. Then, we studied the effects of feature fusion and different data balancing techniques on the best classifier. Figure 1 shows our system architecture.Figure 1
System architecture.


### 2.2.1. Preprocessing

The preprocessing stage aims to set up the images for the subsequent stages. As the images are captured from the camera or scanner, we start by applying noise removal filters, then applying equalization to enhance the contrast of the image. Finally, we select a region of interest from each image to be a candidate for feature extraction.(1) Denoising and Equalization. Image noise is a random distribution of brightness or color information in the image produced when the image is captured by a camera or scanner. Fingerprint images undergo a variety of noises such as random impulsive noise, salt and pepper noise, Gaussian noise, Poisson noise, and so on [25]. After applying a median filter for denoising, we applied a histogram equalizer to enhance image contrast [26]. Original and preprocessed images are shown in Figure 2.Figure 2
Original and preprocessed finger image. (a) Original image and (b) denoised and equalized image.
(a)(b)(2) Region of Interest. The ROI is the more significant region of the fingerprint. Extraction of the ROI begins with detecting the core point, which starts by estimating the orientation field. To detect the orientation, there are many methods. We use the gradient-based method that was proposed in [27]. The algorithm starts by dividing the fingerprint image into nonoverlapping blocks of size w × w. Then, it calculates the gradients gx (x, y) and gy (x, y) for each pixel (x, y) with respect to x and y axis, followed by the calculation of the local orientation. After the orientation field detection, the core point is detected; for any fingerprint, the curvature above the core point is horizontal but is vertical under the core point. A region of size 128 × 128 around the core point is selected, see Figure 3.Figure 3
Finger ROI.(3) Feature Extraction Stage. In this stage, features are extracted from the region of interest. The ROI of the fingerprint can be passed into two domains of feature extraction, either frequency domain or spatial domain or both. We used both FFT and PCA for feature extraction. Then, we applied min-max feature normalization.(4) Fast Fourier Transform. Fourier transform converts the spatial domain input into the frequency domain. We implemented it using the 2-D fast Fourier transform algorithm [8].(1)Fx,y=∑i=0N−1∑j=0N−1fi,je−i2πxiN+yjN,where f(i, j) is an image pixel value.We first applied the FFT to the whole ROI image. Afterward, we divided the power spectrum image into 4 blocks [28]. Because energy distribution is greater at the four corners of the transformed image compared to the middle region, we used further decomposition towards the corners (see Figure 4). Altogether, we divided each block into 10 subblocks, as shown in Figure 5.Figure 4
Fingerprint image power spectrum.Figure 5
Fingerprint image power spectrum subblocks.Afterward, we calculated the mean (2) standard deviation (3) and entropy (4) of each decomposed subblock of size LbX Lb.(2)Meanb=1LbLb∑i=0Lb∑i=0LbFi,j,(3)STDb=1LbLb∑i=0Lb∑i=0LbFi,j−Meanb2,(4)Hb=−∑​pilogpi,where Fi,j is the value of the FFT transform at pixel i,j and pi is the probability of a gray-level i obtained from the normalized histogram of the power spectrum image.Each subimage gives ten nonoverlapping blocks after 4 levels decomposition that yields to a feature vector of size 30 per subblock. So, a feature vector of size 120 is formed for each fingerprint image.(5) Principle Component Analysis. PCA is a statistical technique used in the reduction of data dimensions based on mathematical formulation [14]. First, the ROI images are converted into vectors and stacked in a matrix, see equation (5). Afterward, we applied the eigenvalue decomposition to the covariance matrix of the ROI images (equations (6)–(8)), ordered the eigenvalues in their descending order, and selected the greatest ones with an 80% variance ratio. In Figure 6, shows the decrease of the feature energy. We notice that approximately 160 out of 200 features hold the image energy.Figure 6
Features energy decrease.Finally, each ROI is projected on the more representative eigenvectors corresponding to the selected eigenvalues.(5)ROI=ROI1ROI2…ROIK,(6)M=1NNK∑i=0N−1N−1∑j=0K−1ROIi,j,(7)C=ROI−MROI−MT,(8)C=VΛVT.


### 2.2.2. Min-Max Normalization

To reduce the variability between feature values, we applied normalization. We selected the min-max normalization for our model for each feature value we applied(9)featuren=featuren−minfeaturesmaxfeatures−minfeatures,where minfeatures and maxfeatures are, respectively, the minimal and the maximal values over a feature vector.


### 2.2.3. Classification Stage

The final stage is the classification stage. We used four classifiers: support vector machine (SVM), K-nearest neighbor (KNN), decision tree, and artificial neural network (ANN).The purpose of KNN is to calculate the minimum distance between feature vectors, by computing the distance between the test data and the training data [20]. The decision is made based on the computation of the distance to k neighbours. In our model, we decide for k = 3.SVM creates a hyperplane that separates the two different classes. The SVM classifier exists with different kernel types: linear, RBF, and polynomial. Kernel-type and other SVM parameters can be optimized with hyperparameters optimization techniques [29].A decision tree aims to create a model that is sketchily represented as a tree where each node represents a decision conditions based on the data features.Neural networks are inspired by the learning process occurring in human brains. They consist of an artificial network of functions, called parameters and neurons, which examines the data and passes it from layer to layer. The classification result is given by the last layer neurons’ output. In our work, we established a feedforward neural network model with 20 hidden layers, with scaled conjugate gradient as the training function and the cross entropy for the performance measure during training.Classifier performance is affected by the dataset properties such as dataset size and class imbalance, where the number of observations per class is not balanced. The dataset that we have chosen contains more male fingerprints than female, see Table1. We solved the imbalanced dataset issue by using:(i)
Random undersampling of the majority class: In this sampling technique, we randomly select observations from the majority class.(ii)
Hybrid random oversampling of the minority class and undersampling of the majority class: In this technique, we randomly select observations from the majority class and duplicate observations from the minority class.(iii)
Generation of synthetic minority class data points [30]: synthetic observation of the minority class is produced by synthesizing new samples from the original ones using SMOTE technique.Table 1
Number of female and male images in NIST fingerprint datasets.
FingersFemalesMales(1) Right thumb56338(2) Right index56278(3) Right middle62258(4) Right ring64264(5) Right little56256(6) Left thumb55275(7) Left index55235(8) Left middle69251(9) Left ring66266(10) Left little58244The performance of the classifiers is measured using the (receiver operating characteristic) ROC curve and the accuracy. The ROC curve shows the trade-off between sensitivity and specificity.(10)Accuracy=TP+TNTP+FP+TN+FN,Sensitivity=TPTP+FN,Specificity=TNTN+FP,where TP represents true positive, TN represents true negative, FP represents false positive, and FN represents false negative.


## 2.2.1. Preprocessing

The preprocessing stage aims to set up the images for the subsequent stages. As the images are captured from the camera or scanner, we start by applying noise removal filters, then applying equalization to enhance the contrast of the image. Finally, we select a region of interest from each image to be a candidate for feature extraction.(1) Denoising and Equalization. Image noise is a random distribution of brightness or color information in the image produced when the image is captured by a camera or scanner. Fingerprint images undergo a variety of noises such as random impulsive noise, salt and pepper noise, Gaussian noise, Poisson noise, and so on [25]. After applying a median filter for denoising, we applied a histogram equalizer to enhance image contrast [26]. Original and preprocessed images are shown in Figure 2.Figure 2
Original and preprocessed finger image. (a) Original image and (b) denoised and equalized image.
(a)(b)(2) Region of Interest. The ROI is the more significant region of the fingerprint. Extraction of the ROI begins with detecting the core point, which starts by estimating the orientation field. To detect the orientation, there are many methods. We use the gradient-based method that was proposed in [27]. The algorithm starts by dividing the fingerprint image into nonoverlapping blocks of size w × w. Then, it calculates the gradients gx (x, y) and gy (x, y) for each pixel (x, y) with respect to x and y axis, followed by the calculation of the local orientation. After the orientation field detection, the core point is detected; for any fingerprint, the curvature above the core point is horizontal but is vertical under the core point. A region of size 128 × 128 around the core point is selected, see Figure 3.Figure 3
Finger ROI.(3) Feature Extraction Stage. In this stage, features are extracted from the region of interest. The ROI of the fingerprint can be passed into two domains of feature extraction, either frequency domain or spatial domain or both. We used both FFT and PCA for feature extraction. Then, we applied min-max feature normalization.(4) Fast Fourier Transform. Fourier transform converts the spatial domain input into the frequency domain. We implemented it using the 2-D fast Fourier transform algorithm [8].(1)Fx,y=∑i=0N−1∑j=0N−1fi,je−i2πxiN+yjN,where f(i, j) is an image pixel value.We first applied the FFT to the whole ROI image. Afterward, we divided the power spectrum image into 4 blocks [28]. Because energy distribution is greater at the four corners of the transformed image compared to the middle region, we used further decomposition towards the corners (see Figure 4). Altogether, we divided each block into 10 subblocks, as shown in Figure 5.Figure 4
Fingerprint image power spectrum.Figure 5
Fingerprint image power spectrum subblocks.Afterward, we calculated the mean (2) standard deviation (3) and entropy (4) of each decomposed subblock of size LbX Lb.(2)Meanb=1LbLb∑i=0Lb∑i=0LbFi,j,(3)STDb=1LbLb∑i=0Lb∑i=0LbFi,j−Meanb2,(4)Hb=−∑​pilogpi,where Fi,j is the value of the FFT transform at pixel i,j and pi is the probability of a gray-level i obtained from the normalized histogram of the power spectrum image.Each subimage gives ten nonoverlapping blocks after 4 levels decomposition that yields to a feature vector of size 30 per subblock. So, a feature vector of size 120 is formed for each fingerprint image.(5) Principle Component Analysis. PCA is a statistical technique used in the reduction of data dimensions based on mathematical formulation [14]. First, the ROI images are converted into vectors and stacked in a matrix, see equation (5). Afterward, we applied the eigenvalue decomposition to the covariance matrix of the ROI images (equations (6)–(8)), ordered the eigenvalues in their descending order, and selected the greatest ones with an 80% variance ratio. In Figure 6, shows the decrease of the feature energy. We notice that approximately 160 out of 200 features hold the image energy.Figure 6
Features energy decrease.Finally, each ROI is projected on the more representative eigenvectors corresponding to the selected eigenvalues.(5)ROI=ROI1ROI2…ROIK,(6)M=1NNK∑i=0N−1N−1∑j=0K−1ROIi,j,(7)C=ROI−MROI−MT,(8)C=VΛVT.


## 2.2.2. Min-Max Normalization

To reduce the variability between feature values, we applied normalization. We selected the min-max normalization for our model for each feature value we applied(9)featuren=featuren−minfeaturesmaxfeatures−minfeatures,where minfeatures and maxfeatures are, respectively, the minimal and the maximal values over a feature vector.


## 2.2.3. Classification Stage

The final stage is the classification stage. We used four classifiers: support vector machine (SVM), K-nearest neighbor (KNN), decision tree, and artificial neural network (ANN).The purpose of KNN is to calculate the minimum distance between feature vectors, by computing the distance between the test data and the training data [20]. The decision is made based on the computation of the distance to k neighbours. In our model, we decide for k = 3.SVM creates a hyperplane that separates the two different classes. The SVM classifier exists with different kernel types: linear, RBF, and polynomial. Kernel-type and other SVM parameters can be optimized with hyperparameters optimization techniques [29].A decision tree aims to create a model that is sketchily represented as a tree where each node represents a decision conditions based on the data features.Neural networks are inspired by the learning process occurring in human brains. They consist of an artificial network of functions, called parameters and neurons, which examines the data and passes it from layer to layer. The classification result is given by the last layer neurons’ output. In our work, we established a feedforward neural network model with 20 hidden layers, with scaled conjugate gradient as the training function and the cross entropy for the performance measure during training.Classifier performance is affected by the dataset properties such as dataset size and class imbalance, where the number of observations per class is not balanced. The dataset that we have chosen contains more male fingerprints than female, see Table1. We solved the imbalanced dataset issue by using:(i)
Random undersampling of the majority class: In this sampling technique, we randomly select observations from the majority class.(ii)
Hybrid random oversampling of the minority class and undersampling of the majority class: In this technique, we randomly select observations from the majority class and duplicate observations from the minority class.(iii)
Generation of synthetic minority class data points [30]: synthetic observation of the minority class is produced by synthesizing new samples from the original ones using SMOTE technique.Table 1
Number of female and male images in NIST fingerprint datasets.
FingersFemalesMales(1) Right thumb56338(2) Right index56278(3) Right middle62258(4) Right ring64264(5) Right little56256(6) Left thumb55275(7) Left index55235(8) Left middle69251(9) Left ring66266(10) Left little58244The performance of the classifiers is measured using the (receiver operating characteristic) ROC curve and the accuracy. The ROC curve shows the trade-off between sensitivity and specificity.(10)Accuracy=TP+TNTP+FP+TN+FN,Sensitivity=TPTP+FN,Specificity=TNTN+FP,where TP represents true positive, TN represents true negative, FP represents false positive, and FN represents false negative.


## 3. Results and Discussion

We applied our method on the National Institute of Standards and Technology (NIST) special database 4 gray scale images of fingerprint image groups (FIGS) [31]. The dataset consists of 2000 8-bit grayscale fingerprint image pairs. The size of the PNG images is 512 ∗ 512. The dataset is unbalanced where the number of male fingerprint images is greater than the number of female fingerprint images, Table 1.After preprocessing the images, we extracted both FFT and PCA features. Then we used four classifiers. First, we compared the performance of the four classifiers for our classification problem with each of the previously extracted features. Second, we compared the classification accuracy for each finger using the best classifier among the four initially selected ones using both hybrid random sampling and SMOTE sampling.


### 3.1. Classifiers Comparison

We selected finger 8 for the comparison of the different classifiers because it has more samples than the others for the female class. We compared the four classifiers’ ROC curves when random hybrid sampling is applied; random undersampling of the male class combined with random oversampling of the female and when only random undersampling of the male class is applied. The number of samples per gender is 100. For KNN, SVM, and decision tree, 80% of the samples are used for training and 20% for testing. In the NN classifier, we used 80% training, 10% for validation, and 10% for testing. We also applied hyperparameter optimization for the SVM classifier. The confusion matrix results of the NN are given in Figure7.Figure 7
NN confusion matrices.In Figure8, represents the ROC curves of the four classifiers with FFT and PCA features fusion with hybrid sampling. We notice that the SVM classifier gives the best sensitivity-specificity trade-off, as the SVM ROC curve is the nearest to the highest left corner. In addition, KNN gives the worst ROC curve. This could be explained by an overfitting towards the randomly duplicated class. We also notice that the decision tree gives a better ROC curve than the NN classifier; the number of samples is small for training an NN. In the opposite, the decision tree gives a good ROC curve showing that it has sensitivity better than NN.Figure 8
ROC curve with concatenated features and male undersampling and female oversampling.Then we compared the four classifiers’ ROC performance when only undersampling was applied to balance the dataset with the lowest class size. From Figure9, we notice that SVM gives the best ROC. We notice that the performance of NN decreased in this case. This could be explained by the fact that NN is sensitive to the size of the dataset. The area under the ROC (AUC) curve was calculated for the four classifiers under both undersampling and oversampling and for the FFT, PCA, and fused features in Table 2. We notice that the decision tree AUC with random undersampling is better than its AUC with hybrid sampling. On the other hand, SVM is slightly improved with hybrid sampling.Figure 9
ROC curve with concatenated features and undersampling.Table 2
AUC comparison.
KNNSVMDecision treeNNUndersamplingFFT86.5199.1499.9583.95PCA87.2699.9899.9372.63Fusion90.3899.2799.9388.57Hybrid samplingFFT88.2999.8398.9187.02PCA90.6999.9999.5290.83Fusion89.8099.9999.6892.25We emphasize here that SVM is showing its robustness, consistency, and effectiveness as it can perform very well under both undersampling and combined undersampling and oversampling compared to the other three classifiers. So, in the next section, we will focus on the performance of the SVM classifier.


### 3.2. Fingers Comparison

We compared the performance of the SVM classifier for different fingers. We balanced the dataset with 100 female fingerprints and 100 male fingerprints for each finger. We used both random sampling and SMOTE techniques. Moreover, to overcome the limited number of samples per class, we applied 10-fold cross validation.In Table3, shows the accuracy of the SVM classifier on the FFT features with random hybrid sampling and with SMOTE sampling. We notice that the best identification of males and females are given by finger 7—left index—for both random sampling and SMOTE. The highest male identification accuracy of 73.26% is reached by SMOTE, and the highest female identification is found for random oversampling with 90.36%.Table 3
SVM accuracy with FFT feature extraction, FFT.
RandomSMOTEMalesFemalesMalesFemales164.1088.4466.6983.67269.0888.4370.5786.09367.6384.8468.5384.80467.6783.3865.9484.27569.3689.6469.9187.42659.3687.8259.6381.25771.5890.3673.2687.99868.7681.4870.1683.98960.5180.4164.6881.891069.7089.4769.5487.14Average66.7886.4367.984.85The bold values show the best accuracy for gender identification.In Table4, shows the accuracy of the SVM classifier with PCA features. We notice also that the best identification of male and female are given by different fingers. Female is best identified by the right thumb when random sampling is used and by the right Ring when SMOTE sampling is used. On the other hand, male gender is best identified by right ring when random sampling is used and the left little when SMOTE is used. In Table 5, shows the accuracy of the SVM classifier when both FFT and PCA features are fused. We notice that the highest accuracy for both male and female is given by SMOTE sampling, with 75.55% correct identification of the male gender and 91.62% correct identification of the female gender.Table 4
SVM accuracy with PCA feature extraction.
RandomSMOTEMalesFemalesMalesFemales163.8887.8463.5086.26270.2180.8574.8277.71366.6779.6874.1576.56470.7573.6773.4986.58562.4783.8570.7774.53661.8287.3266.9381.06757.9184.7069.4276.43866.5064.7476.4965.98964.3364.7863.3173.971062.9381.2078.0670.14Average64.7578.8671.176.92Table 5
SVM accuracy with FFT and PCA fusion.
RandomSMOTEMalesFemalesMalesFemales167.2787.5865.7786.10272.8087.9473.9188.76373.2485.8674.2588.72473.4183.3575.5591.62572.9389.6173.2088.23664.3188.2966.1884.49770.3789.1472.5989.64869.4679.9371.0486.48964.8179.1267.7785.661067.5286.4171.3586.55Average69.6185.72371.1687.63With FFT and PCA fusion, finger 4 gives the best accuracy for male identification and finger 5 gives the best accuracy for female identification when random sampling is used. With SMOTE, finger 4 gives the best accuracy for male and female.


## 3.1. Classifiers Comparison

We selected finger 8 for the comparison of the different classifiers because it has more samples than the others for the female class. We compared the four classifiers’ ROC curves when random hybrid sampling is applied; random undersampling of the male class combined with random oversampling of the female and when only random undersampling of the male class is applied. The number of samples per gender is 100. For KNN, SVM, and decision tree, 80% of the samples are used for training and 20% for testing. In the NN classifier, we used 80% training, 10% for validation, and 10% for testing. We also applied hyperparameter optimization for the SVM classifier. The confusion matrix results of the NN are given in Figure7.Figure 7
NN confusion matrices.In Figure8, represents the ROC curves of the four classifiers with FFT and PCA features fusion with hybrid sampling. We notice that the SVM classifier gives the best sensitivity-specificity trade-off, as the SVM ROC curve is the nearest to the highest left corner. In addition, KNN gives the worst ROC curve. This could be explained by an overfitting towards the randomly duplicated class. We also notice that the decision tree gives a better ROC curve than the NN classifier; the number of samples is small for training an NN. In the opposite, the decision tree gives a good ROC curve showing that it has sensitivity better than NN.Figure 8
ROC curve with concatenated features and male undersampling and female oversampling.Then we compared the four classifiers’ ROC performance when only undersampling was applied to balance the dataset with the lowest class size. From Figure9, we notice that SVM gives the best ROC. We notice that the performance of NN decreased in this case. This could be explained by the fact that NN is sensitive to the size of the dataset. The area under the ROC (AUC) curve was calculated for the four classifiers under both undersampling and oversampling and for the FFT, PCA, and fused features in Table 2. We notice that the decision tree AUC with random undersampling is better than its AUC with hybrid sampling. On the other hand, SVM is slightly improved with hybrid sampling.Figure 9
ROC curve with concatenated features and undersampling.Table 2
AUC comparison.
KNNSVMDecision treeNNUndersamplingFFT86.5199.1499.9583.95PCA87.2699.9899.9372.63Fusion90.3899.2799.9388.57Hybrid samplingFFT88.2999.8398.9187.02PCA90.6999.9999.5290.83Fusion89.8099.9999.6892.25We emphasize here that SVM is showing its robustness, consistency, and effectiveness as it can perform very well under both undersampling and combined undersampling and oversampling compared to the other three classifiers. So, in the next section, we will focus on the performance of the SVM classifier.


## 3.2. Fingers Comparison

We compared the performance of the SVM classifier for different fingers. We balanced the dataset with 100 female fingerprints and 100 male fingerprints for each finger. We used both random sampling and SMOTE techniques. Moreover, to overcome the limited number of samples per class, we applied 10-fold cross validation.In Table3, shows the accuracy of the SVM classifier on the FFT features with random hybrid sampling and with SMOTE sampling. We notice that the best identification of males and females are given by finger 7—left index—for both random sampling and SMOTE. The highest male identification accuracy of 73.26% is reached by SMOTE, and the highest female identification is found for random oversampling with 90.36%.Table 3
SVM accuracy with FFT feature extraction, FFT.
RandomSMOTEMalesFemalesMalesFemales164.1088.4466.6983.67269.0888.4370.5786.09367.6384.8468.5384.80467.6783.3865.9484.27569.3689.6469.9187.42659.3687.8259.6381.25771.5890.3673.2687.99868.7681.4870.1683.98960.5180.4164.6881.891069.7089.4769.5487.14Average66.7886.4367.984.85The bold values show the best accuracy for gender identification.In Table4, shows the accuracy of the SVM classifier with PCA features. We notice also that the best identification of male and female are given by different fingers. Female is best identified by the right thumb when random sampling is used and by the right Ring when SMOTE sampling is used. On the other hand, male gender is best identified by right ring when random sampling is used and the left little when SMOTE is used. In Table 5, shows the accuracy of the SVM classifier when both FFT and PCA features are fused. We notice that the highest accuracy for both male and female is given by SMOTE sampling, with 75.55% correct identification of the male gender and 91.62% correct identification of the female gender.Table 4
SVM accuracy with PCA feature extraction.
RandomSMOTEMalesFemalesMalesFemales163.8887.8463.5086.26270.2180.8574.8277.71366.6779.6874.1576.56470.7573.6773.4986.58562.4783.8570.7774.53661.8287.3266.9381.06757.9184.7069.4276.43866.5064.7476.4965.98964.3364.7863.3173.971062.9381.2078.0670.14Average64.7578.8671.176.92Table 5
SVM accuracy with FFT and PCA fusion.
RandomSMOTEMalesFemalesMalesFemales167.2787.5865.7786.10272.8087.9473.9188.76373.2485.8674.2588.72473.4183.3575.5591.62572.9389.6173.2088.23664.3188.2966.1884.49770.3789.1472.5989.64869.4679.9371.0486.48964.8179.1267.7785.661067.5286.4171.3586.55Average69.6185.72371.1687.63With FFT and PCA fusion, finger 4 gives the best accuracy for male identification and finger 5 gives the best accuracy for female identification when random sampling is used. With SMOTE, finger 4 gives the best accuracy for male and female.


## 4. Conclusion and Future Directions

Gender identification is needed in applications such as forensics. In this work, we compared the performance of four classifiers: KNN, SVM, decision tree, and feedforward neural networks. We showed that the SVM classifier demonstrated the best accuracy with high robustness. SVM presented high accuracy on both balanced dataset with under-random sampling and balanced dataset with hybrid sampling. We compared the performance of SVM with FFT features, PCA features, and feature fusion. In addition, we applied both random sampling and SMOTE sampling techniques. We showed that SMOTE gives the best performance for feature fusion, with a highest accuracy for female identification of 91.62% and a male identification rate of 75.55% for the right ring finger. We emphasize that our work investigated the use of fingerprint-based gender identification on a small dataset with imbalanced classes, where balancing was applied through two techniques, random hybrid sampling, and SMOTE. In the future, we plan to apply other balancing techniques. Also, we plan to integrate our gender identification with a high-performance computing (HPC) system in order to decrease the search space and search time in fingerprint-based identification systems [6, 32].



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*Source: 1626953-2022-12-31.xml*