Developing a LBPH-based Face Recognition System for Visually Impaired People

Abstract

A large number of people around the world are suffering from visual impairment which is a global health issue. These visually challenged people face a great deal of difficulties in carrying out their day-to-day activities. Recognizing a person is one of the major problems faced by them. This document represents a face recognition system with auditory output which can be beneficial for visually challenged people in recognizing known and unknown persons. Proposed face recognition system is comprised of three main modules including dataset creation, dataset training, and face recognition. Here, Haar Cascade Classifier is used to detect face from a live video stream and then Local Binary Pattern Histogram (LBPH) algorithm is applied to create the recognizer for face recognition using OpenCV-Python library. This system can detect and recognize multiple people and is also capable of recognizing from both front and side face. The overall face recognition accuracy is about 93%. Apart from visually challenged people, old people with Alzheimer’s disease can also be benefited using this system.