# Visual Static Hand Gesture Recognition Using Convolutional Neural Network
## 1 Introduction  
In order to communicate with computers in more effective and natural ways, researchers have experimented with various methods for more than half of the last century. Over time, human interaction was made mainly via a keyboard and mouse. Most of the time, we interact with computers using our fingers and eyes, but other body parts, including our legs, arms, and mouth, are underutilized or never used at all. This is inconvenient since it is like composing emails with just one finger. Standard image processing methods do not produce excellent results, so machine learning is required for gesture detection to reach its full potential. Gestures are meaningful, expressive body motions that involve physical movements of the body, hands, arms, head, face, and fingers. Gesture recognition is the process that seeks to identify gestures and translate them into commands that can facilitate effective communication between humans and computers. Hand gestures are either dynamic or static. Implicit hand gestures and postural behavior are considered for the recognition of emotional states [1, 2]. However, this paper is meant to focus more on the recognition of static, explicit hand gestures. At present, there are some problems in visual gesture recognition, such as accuracy, real-time, and poor robustness. Although there are many methods of gesture recognition,vision-based gesture recognition still faces many serious problems in practice. It is mainly reflected in a low recognition rate, poor robustness, insensitivity in real-time, and poor practicability. Gesture recognition should bring great results no matter the background; it should work whether you are in the car, at home, or walking down the street. Using \(CNN\) helps overcome the problem of identifying gestures in complex backgrounds with very high accuracy. Advancements in \(CNN\) and the recently emerged deep learning techniques avoid the need for deriving complex handcrafted feature descriptors from images, so they outweigh the classical approach to hand gesture recognition [3; 4; 5; 6]. By learning the high-level abstractions in images, \(CNN\) automates the feature extraction process and uses hierarchical architecture to capture the most discriminative feature values [7; 8]. In ref. [9], [PERSON] et al. proposed a parallel \(CNN\) model that used \(RGB\) and depth images as input. Parallel \(CNN\) consists of two \(CNN\)s, namely depth-\(CNN\) and \(RGB\)-\(CNN\), where one takes a depth image as input while the other takes an \(RGB\) image as input. A prediction probability was weighted on the last layer of each \(CNN\) output and concatenated to have the input to a softmax classifier layer. The dataset used contains a total of 24 hand gestures, representing the 24 letters (except J and Z, because J and Z are dynamic hand gestures). Each gesture contains 5000 sample images, which are 2500 RGB images and 2500 depth images, completed by 5 people in different backgrounds with different illumination. So there are a total of 120,000 pictures, which are 60,000 RGB images and 60,000 depth images. The model accuracy has reached 93.3% for American Sign Language (\(ASL\)). The advantage of this approach is its ability to capture both appearance and depth information. In ref. [10], [PERSON] et al. proposed \(CNN\) with four convolutional layers, with a max-pooling layer affixed to each convolutional layer, followed by a fully connected layer and a softmax classifier. The proposed \(CNN\) was able to attain 99% for Irish Sign Language (ISL). The dataset was collected by filming human subjects performing ISL hand shapes and movements and then extracting frames from the videos. This produced a total of 52,688 images for the 23 common hand- shapes from ISL. The very high accuracy can be explained by the simple image black backgrounds, which make it so easy and unchallenging to identify images and gestures. However, this method may struggle when applied to real-world scenarios with complex backgrounds and lighting variations, which is a disadvantage.