## 1 Introduction  
At present, with constant developments in information technology, human beings are attempting to communicate with computers in more natural ways. The traditional input devices for human-computer interaction (HCI), such as mice, keyboards, remotes, and so on, no longer serve as natural means of interaction owing to their lack of flexibility. In general, the natural ways for humans to interact with computers include voice commands and body language, and these are available in many commercial electronic products. Although speech recognition is one of the natural methods for human-computer interaction, it is limited by noisy environments and the different ways that people pronounce the same words. Another human-computer communication method involves body language-based interaction. In many cases, interacting with a computer through body language is more reliable than other methods. Body language can involve different types of expression, such as hand gestures, body poses, and facial emotions. Compared with gestures with other body parts, hand gestures are the most efficient means of meaningful expression and serve as a common language for all humans. For example, waving one's hand to express 'hello' is pretty flexible.  
Hand gesture recognition is one method of interaction for human beings. Hand gestures are defined as movements of the human hand that are used to express meaningful information and thoughts. We use our hands every day while talking to present ideas, points at people or things, and so on. Everyone is familiar with hand gesture interaction. It has even been suggested that hand gestures will soon replace the touchscreen technology of mobile phones and other devices. In HCI systems, hand gesture-based interfaces are broadly applied in many practical applications, such as sign language recognition [1], robot control [2], virtual mouse control [3; 4; 5; 6; 7; 8], exploration of medical image data [9], human-vehicle interaction [10], and immersive gaming technology [11]. Therefore, the recognition of human hand gestures from videos has been one of the main goals of computer vision for decades, particularly with traditional cameras (RGB cameras).  
However, even when using RGB cameras, most existing algorithms tend to fail when faced with changing light levels, complex backgrounds, different user-camera distances, multiple people, or background or foreground movements during the hand tracking. Microsoft's Kinect RGB with depth (RGB-D) camera has extended depth-sensing technology and interfaces for human-motion analysis applications.  
More recently, systems using convolutional neural networks (CNNs) have shown outstanding performance in HCI [12]. Owing to its use of multiple layers, CNNs' architecture is built for automating feature learning. Therefore, through the use of a deep learning framework, data are represented by a hierarchy of features arranged from low-level to high-level.  
In this paper, the main goal of our system is to discover a hand gesture-based interface from depth videos. We present a combination of fingertip detection and a 3D convolutional neural network for hand gesture recognition from a depth camera. The proposed network architecture effectively exploits multi-level features to generate high-performance predictions.  
The remainder of the paper is organized as follows. In the second section, we present our proposed approach and the related materials that we used in this paper. The architecture of the proposed method is then discussed in Section 3. Comprehensive experiments on a challenging dataset are used to validate the effectiveness of our system in Section 4. Finally, in Section 5, the conclusion and future works are presented.