# Recursive Feature Elimination for Improving Learning Points on Hand-Sign Recognition
## 1 Introduction  
Hand gestures and poses allow us to communicate non-verbally through sign language. It is the primary communication method for the deaf and hard-of-hearing communities. The importance of sign language is increasing as these groups grow. However, learning sign language is a challenging skill that requires much practice.  
Due to the increasing importance of hand signs in daily life, researchers have used many methods to detect automatic hand signs. Convolutional Neural Network (CNN) [1, 2, 3, 4, 5] is one of the most used methods used to extract features from hand images. For the classification and detection, machine learning techniques, such as Support Vector Machine (SVM) [1, 6, 7, 8, 9], K-Nearest Neighbor (KNN) [7, 10], and Light Gradient Boosting Machine (Light GBM) [9], are used.  
This study used Mediapipe [11] for feature extraction of hand images. Mediapipe can extract 21 hand landmarks on each hand with minimum resource usage. Hand landmarks are used for hand-pose detection in this research, since they can reduce the interference from the image background and give better detection results by using a smaller number of parameters compared to traditional pixel-based detection using CNN. Nowadays, feature selection is becoming more important, especially in datasets containing many variables and features. Feature selection helps in eliminating irrelevant factors, which can improvethe accuracy of the classification process as well as its performance. There are several important factors that highlight the importance of feature selection. To start, it simplifies the model in such a way that fewer parameters are required. Next, it can reduce the time spent in training, reduce overfitting by increasing generalization, and avoid dimensional traps [12]. Recursive Feature Elimination (RFE) will be used for feature selection, while neural networks will be used for the classification model. The digit sign language type used in this study is American Sign Language (ASL), mainly used in English-speaking countries.  
This study tries to answer three questions, which are: (1) Are all 21 hand landmarks extracted using Mediapipe useful in recognizing digits in ASL? (2) Which hand landmarks are more important in detecting ASL digits? (3) Can we improve the hand-sign detection accuracy by only using the more important hand landmarks?  
The main contributions of this research are as follows: (1) To find the 10 essential hand landmarks in detecting ASL digit hand signs. (2) This shows that not all hand landmarks have the same importance in digit hand-sign detection; using only the essential landmarks can improve detection accuracy. (3) We also proposed using a novel distance from the hand landmark to the center of the palm to perform feature selection in order to address the limitation of the RFE implementation in the python library.  
This research consists of five sections. Section 2 presents some research related to hand-sign detection and Section 3 describes the methodology of this research. Presentation and discussion of the experiment results are given in Section 4. Section 5 will provide the conclusion and present future works from this study.