# Extracting Fingerprint Features Using Autoencoder Networks for Gender Classification
## 6 Conclusions  
This paper proposes an effective network considering the global reception field in the gender classification task, which is realized by replacing normal convolutions with dilated convolutions in the extraction method. The experiment thoroughly explores efficiency from three aspects. First, we compare our method with various methods with fair implementation details in our dataset. Six typical automatic feature extraction methods like DWT, SVD, VGG, and our method coupled with nine mainstream classifiers such as Adaboost, kNN, SVM, CNN, etc., a total of 54 combinations are evaluated. The experimental results  
Figure 6: Overall heatmaps.  
reveal that the combination of our extractor with the CNN classifier outperforms other combinations. For average accuracy, it reaches 96.50% and for separate gender accuracy, it reaches 0.9752 (males)/0.9548 (females). Second, we investigate the effect of fingers by classifying gender using separate fingers and find the best-performing finger is the right ring finger, which reaches an accuracy of 92.455%, as shown in Figure 7. Third, we study the effect of features by visualizing the concentrations of fingerprints. Depending on the analysis, loops/whorls (level 1), bifurcations (level 2), and line shapes (level 3) may have a close relationship with gender, as shown in Figure 8.  
Figure 8: This study visualized the heat map through the grad-cam technique and found that the concentration was mainly on or around the center of the fingerprint, with the concentration area tending to be more of a continuous section, and it can be said that the basis of recognition is the complete ridge feature, thus, also confirming the reason for the manual extraction of features and also validating the interpretability of this paper.  
Figure 7: Estimate performance of each finger (%), in which \(F_{x}\) represents Finger \(x\). The index from 1 to 5 means little finger, ring finger, middle finger, index finger, and thumb, respectively. In addition, the \(F_{average}\) represents the average of their fingers. **The highest average accuracy is index finger at 91.413% and the lowest is little finger at 83.340%.**  
**Author Contributions:** Conceptualization, Y.Q.; data curation, M.Q.; formal analysis, F.W. and H.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the serving local special projects of the education office of Shaanxi province, China [grant number No.22]C019] and China Student Innovation and Entrepreneurship Training Program [grant number S202110708067].  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Shaanxi University of Science and Technology (710021, 31 August 2022).  
**Informed Consent Statement:** This study does not involve any informed consent issues since there all the data used in this paper is simulated.  
**Data Availability Statement:** If anyone wants the data availability of this paper, please send an e-mail to [EMAIL_ADDRESS].  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Abbreviations**  
DDC-ResNet Dense Dilated Convolution ResNet  
CAM Class Activating Mapping  
DWT Discrete Wavelet Transformation  
SVD Singular Value Decomposition  
PCA Principal Component Analysis  
FFT Fast Fourier Transform  
VGG Visual Geometry Group  
2D-DCT 2-Dimension Discrete Cosine Transform  
PSD Power Spectral Density  
LBP Local Binary Pattern  
LPQ Local Phase Quantization  
CNN Convolution Neural Network  
BERT Bidirectional Encoder Representations from Transformers  
LSTM Long Short-Term Memory  
SVM Support Vector Machines  
kNN K-Nearest Neighbor  
ID3 Iterative Dichotomiser 3  
LDA Linear Discriminant Analysis