# A Novel Approach to Wearable Image Recognition Systems to Aid Visually Impaired People
## 1 Introduction
However, a group of researchers [18] present that it is technically possible to develop a simple real-time face recognition system in a wearable device with low processing power. And they designed the Samsung Galaxy Gear, a smartwatch device that can recognize and register faces. But the problem is that in the face detection process, the user and the camera must be still for a few seconds before the next recognition process, which undoubtedly brings inconvenience to the user. Moreover, in the system test, the identification accuracy was only 83.64%. In another study [19], the author developed a head-mounted wearable device equipped with a stereo camera, which can provide navigation service and object recognition for the blind. The system sends the video sequence collected by the camera to the cloud server through the smartphone for path planning and object recognition. However, the depth information required for the simultaneous localization and mapping (SLAM) algorithm is usually large, and it takes some time to transmit to the cloud platform, so the real-time performance will be affected. Furthermore, the device relies on smartphone support, so the controller itself cannot complete the upload service. Recently, some researchers have proposed a Kinect-based wearable face recognition system [20], which uses a Microsoft Kinect sensor to collect facial information and perform face detection. The system can generate a sound associated with the identified person, virtualizes at his or her estimated 3-D location, making users feel that the source of sound is the location of the target. This approach is superior to the traditional face detection method but does not support object and text recognition. With the development of artificial intelligence technology, there have been some commercialized products. The emerging smart devices such as OrCam myeye2 [21] and eSight [22], combined with image recognition technology, can help visually impaired people identify faces, texts and products, but they do not support visual path navigation. Oxsight [23] captures the shape and distance of objects in the surrounding environment through a 3D camera, enhances the objects by highlighting, and outputs them to the mini screen of glasses. However, Oxsight can only be seen by some people with mild vision impairment, so it is hopeless for the blind. Additionally, these commercial devices are very expensive. OrCam MyEye2 is worth $4491.9 and eSight costs $5950. However, according to the survey, about 90% of visually impaired people come from developing countries. The price will make them unaffordable. OrCam MyEye2 is also powered by a mobile power source, which undermines the portability of the wearable device. The battery life of eSight is only 2 h.  
This paper aims to develop a low-cost and effective wearable smart glass, which can help users find the right people, identify common objects in life, and read paper texts. The main contribution of this paper is to propose a low-cost solution for cloud image recognition and local human-computer interaction. This method can overcome the disadvantages of expensive smart devices such as OrCam Myeye2 and ensure the accuracy and reliability of identification while ensuring low-cost and low power consumption. The entire system is deployed on a pair of lightweight and wearable glasses, to provide sound feedback and navigation for visually impaired people. The performance comparison between the system and other auxiliary equipment is shown in Table 1.  
The rest of this article is organized as follows: Section 2 presents the hardware configuration of the smart device and overall system architecture. Section 3 describes the core algorithm and mathematical model of the system. Section 4 shows some test results and proves the effectiveness of the intelligent system. Finally, some conclusions are drawn in Section 5.