# Deep-Learning-Based Cognitive Assistance Embedded Systems for People with Visual Impairment
## 2 Related Work  
### Face Recognition  
Face recognition is a key application of computer vision. The most advanced face recognition techniques, such as VGGFace [22], FaceNet [23], and ArcFace [24], have been made possible by the recent advancements in deep learning and CNN architectures. Researchers have also investigated the use of IoT technologies and facial recognition in health care. For example, a real-time facial recognition system for assisting people with visual impairment was suggested by [PERSON] et al. [41]. Their system uses the local binary pattern histogram (LBPH) algorithm to identify human faces in videos captured by a smartphone. However, the system can only identify one human face in each frame, and the LBPH algorithm requires the input images to be converted into the binary or grayscale format.  
Cloud-based techniques have enabled considerable improvements in system performance. A mobile face recognition system for helping people with visual impairments was presented by [PERSON] and [PERSON] [25]. This system was implemented on a smartphone that connected to a server. The primary system operations, such as facial detection and recognition, were implemented by the smartphone. However, the identification and enrolling of a new person was performed by the server. The Cascade classifier and the LBPH algorithm were used to find and identify faces. [PERSON] et al. [26] proposed a smart wearable cloud-based system that can identify text, objects, and human faces to aid people with visual impairment in effectively perceiving the world. This system contains two key cooperative processing components: a cloud server and a local unit. The local unit captures input photographs, communicates with the server, and provides feedback. The cloud server primarily performs image processing and sends its results to the local unit. However, the aforementioned system can only be used in locations with Internet access because of its reliance on the cloud server.  
Computer-based face recognition methods have high flexibility and practical applicability. [PERSON] et al. [27] introduced a real-time system (DEEP-SEE FACE) to facilitate cognition, interactions, and communication for people with visual impairment. This system combines computer vision algorithms and deep CNNs to enable those with visual impairment to detect, follow, and identify numerous people in front of them. A laptop serves as the system's processing unit and handles all tasks, including the collection of input images, the processing of the images, and the exporting of the results. However, those with visual impairment face difficulty in moving with the device over time because of its weight. [PERSON] et al. [28] suggested a wearable system for face recognition to aid people with visual impairment. They used a Kinect sensor, which contains an infrared (IR) emitter and a red-green-blue (RGB) camera, to collect RGB-Depth images. They then combined several methods, including the histogram of oriented gradients (HOG), principal component analysis, and k-nearest neighbor, to identify faces. However, the system cannot be used outdoors because it requires Kinect's IR sensor.