# Fingerprints Classification through Image Analysis and Machine Learning Method
## 2 Related Work  
Machine learning is increasingly proving its important in areas of practical application [11]. Therefore, scientists are researching and developing machine learning methods that are becoming increasingly optimal. One of the applications that is attracting growth is fingerprint classification. An approach to the fingerprint classification problem is researched in Reference [12]. The fingerprint is segmented into regions, this work will reduce the variance of the element directions. A relation graph is build based on the segmentation of the directional image. A model graph is used to compare with the obtained graph, can be adapted to graph matching techniques. Reference [13] presented a fingerprint classification system and its performance in an identification system used a fuzzy-neural network classifier. The classification scheme is based on fingerprint feature extraction, which involves encoding the singular points together with their relative positions and directions obtained from a binaries fingerprint image. Image analysis is carried in four stages, namely segmentation, directional image estimation, singular-point extraction and feature encoding. A method is proposed in Reference [14] based on analysis singularities and ridges relating singular points. Because of low quality images, it is very difficult to get correct positions of singular features. The authors used analysis ridge tracing and curves features to classify fingerprints. A machine learning algorithm that takes a robust approach to the classification of fingerprints is SVM. This method has created a highly accurate classification system. The advantage of the algorithm is shown in the classification of fingerprints into multiple classes. The authors of Reference [15] used a combination of the SVM algorithm with the naive Bayes method to classify fingerprints based on the number of core and delta points on fingerprints. Many researchers have tried to extract singular points in the flow of the ridges [16]. The authors of References [17; 18; 19] proposed a heuristic algorithm with singularities to classify fingerprints; the disadvantage of these studies is not focusing on improving image quality. Because they use features of singularity points position. This leads to a great influence on the accuracy of the system. The Galton-Henry classification scheme is used to classify fingerprints. This method is presented in Reference [20]. They use rotation-invariant distance, then match this distance between the FingerCode sample set with the new fingerprint pattern. During the classification process, the rotation-invariant distance extraction takes place in parallel with the training process. This gives the advantage of fast system time. The Random Forest (RF) algorithm is used to handle large amounts of data and multi-class problems in classification [21]. This algorithm is an ensemble of randomized decision trees that resolves a regression task [22] and a classification task [23; 24; 25] The most prominent application of random forest is the detection of human body parts from depth data [26]. This application demonstrates the practicability of random forests for machine learning problems in the real world.  
There are many known ways to improve the performance of fingerprint classification systems. In this paper, we proposed a combination of computer vision algorithm and traditional machine learning methods to treat such problems, which we have successfully research in other fields [27; 28; 29]. Moreover, we also designed and implemented image processing methods with denoising and increasing edge using the Canny method.