# A Novel Approach to Wearable Image Recognition Systems to Aid Visually Impaired People
## 1 Introduction  
Visual impairment is a common health problem in different age groups. According to the World Health Organization (WHO) Fact Sheet of 2018, 253 million people [1] in the world are estimated to be visually impaired (VI), among which 36 million are blind and 217 million suffer from moderate to severe visual impairment. Action and cognition are the two major challenges that most visually impaired people (VIP) have in their daily lives. The corresponding solutions are navigation and image recognition. In short, if the navigation and image recognition technology can be integrated into a wearable device [2], it can greatly alleviate the current difficulties of visually impaired people. Most of the assistive systems for the visually impaired collect visual information through various sensors, and then convert to auditory or tactile information [3; 4; 5]. However, the research of the assistive system is more oriented towards navigation, which is mainly to help the users solve the problem where they are and where they would go. For instance, [PERSON] et al. [4] proposed a TactiPad device in the shape of a cube with an edge length of 8 cm weighting only 200 g. The TactiPad is mainly used for obstacle detection and a tactile gist display. Its surface can be manually explored by unconstrained hand movement. [PERSON], [PERSON] et al. have proposed a NavGuide system [6] that contains six ultrasonic sensors to detect obstacles in different directions. The system also helps the visually impaired to turn left, right, front and avoid wet floors. [PERSON] et al. [7] designed a blind-guide crutch based on multiple sensors, which uses ultrasonic sensors to avoid obstacles and sends out alarm information in the form of vibration and voice. External GPS receiver [8; 9] can also be used to improve the locating accuracy, But, this system can only be applied outdoors. In addition to an ultrasonic sensor [10], RFID (Radio Frequency Identification) sensor [11; 12], RGB-D camera [13] and Kinect depth camera [14] are also available as sources of visual information. But RFID systems are expensive and require indoor installation in advance, so are not adaptable to unfamiliar environments. Images captured by RGB-D cameras and Kinect cameras are converted into deep images, which consume a lot of computing resources during processing. It is improper for low-power devices. Therefore, this paper uses an ultrasonic sensor as the main obstacle avoidance sensor. However, wearable devices that do not support object recognition are difficult to meet the daily needs of the visually impaired. They cannot know the object information of the target in front (whether it is a person or a thing, and what is the specific name). In view of this, researchers have shifted the focus of auxiliary equipment to object recognition. As early as 2005, [PERSON] et al. [15] developed a pair of glasses for real-time facial recognition. They selected principal component analysis (PCA) algorithm for face recognition, with high accuracy under different facial posture and illumination angles. However, its processor is still an external small computer, so the whole system is not a perfect wearable device. [PERSON] et al. [16] proposed a smart white cane that can be used for facial and expression detection. The algorithm is based on a commercial solution FaceSDK to match images to a user-built database and provide real-time audio feedback. However, the cane does not consider low power consumption. [PERSON], [PERSON] et al. [17] designed a wearable facial recognition device, which consists of a camera, a head mounted display (HMD) and a computer. The system detects faces by using the Adaboost algorithm proposed by [PERSON]. However, it can only detect and track a human face, so it is useless when the user meets more than two people. In addition, the use of desktop PC reduces the mobility of the device. Most image recognition systems typically require high-performance hardware.