# Extracting Fingerprint Features Using Autoencoder Networks for Gender Classification
## 2 Related Work  
Previous discussions have demonstrated that gender-related features have a significant impact on fingerprint gender identification. In this section, we will review the papers according to the extraction method of fingerprint gender features. **Manual feature extraction**. Manual feature extraction was the earliest method of fingerprint gender feature extraction. Initially, it was found that gender could be identified by the ridges of the fingerprint epidermis [11; 15; 16]. [PERSON] et al. manually calculated the number of ridges in 66 regions of the fingerprint epidermis and demonstrated that the number of ridges can determine gender [15]. [PERSON] found a ridge density threshold of 13 ridges/25 mm\({}^{2}\), which determines gender as male when the ridge density is below  
Figure 1: General automatic gender classification methods preview. **(a)** Traditional method for gender feature extraction; **(b)** Deep learning method for gender feature extraction; **(c)** We propose a feature extraction method of fingerprint that takes global features into account. the threshold [16]. In other words, females have a higher ridge density, possibly because they have a lower ridge width [11]. Owing to the low accuracy of manually extracted fingerprint epidermal features, [PERSON] proposed manual extraction of ridge counts, ridge thickness to valley thickness ratio (RTVTR) and white line counts and used fuzzy CMEans, Linear Discriminant Analysis and neural networks as classifiers to determine gender. The method used manual extraction of fingerprint epidermal features and classification using an automatic classifier to obtain accuracies of 80.39%, 86.5%, and 88.5%, respectively [10]. **Automatic feature extraction**. Due to the inevitable error and high manpower consumption of manual feature extractions, automatic fingerprint extraction has been proposed. In 2012, ridge features have been analyzed in the spatial domain using FFT, 2D-DCT, and PSD [12], reaching an accuracy of 90% for females and 79.07% for males, respectively. In the same year, [PERSON] proposed a method using DWT and SVD as the feature extractor [3]. In 2014, [PERSON] used LBP and LPQ on texture features and used PCA to reduce the features, then the kNN classifier was applied to the extracted features [13]. The author reached 88.7% prediction rate. **Deep learning feature extraction**. Due to the development of convolutional neural networks, deep learning has been widely used in recent years for feature extraction tasks, especially in the field of biometrics. Initially, people used a combination of automatic feature extraction and neural networks. [PERSON] used the discrete wavelet transform to extract fingerprint features, which were then output to a back propagation neural network for gender classification, ultimately achieving a classification accuracy of 91.45% [4]. To obtain better performance, biometric feature extraction was performed entirely using deep learning methods. [PERSON] used CNN models, specifically ResNet-34, trained with transfer learning techniques to complete fingerprint feature extraction and achieved a combined classification accuracy of 75.20% [17]. In manual biometric feature extraction tasks, most require a priori knowledge. Since the existence of deep convolutional self-encoder neural networks, [PERSON] et al. have proposed an autoencoder-based framework to extract feature information from medical images due to the autoencoder's ability to automatically extract important essential features from the data. In recent years, many studies have shown that autoencoders have shown outstanding performance in feature extraction tasks, especially in the field of biometrics [14]. In addition, the combination of encoder and classifier also achieved satisfactory results on many deep learning tasks. [PERSON] et al.