An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE

TL;DR

This paper introduces an enhanced real-time low-resolution face recognition system using LBPH and CLAHE, achieving high accuracy even at 15 pixels resolution, suitable for surveillance and law enforcement applications.

Contribution

The study develops a low-resolution face recognition system with improved accuracy at 15 pixels by combining LBPH, CLAHE, and face alignment, and introduces datasets LRD200 and LRD100.

Findings

Achieves 78.40% accuracy at 15 px with 200 images per person

Achieves 60.60% accuracy at 15 px with 100 images per person

System maintains over 70% accuracy with 30-degree facial deflections

Abstract

This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px and 98.05% at 45 px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15 px and 95% at 45 px respectively. A facial deflection of about 30 degrees on either side from the front face showed an average face recognition precision of 72.25% - 81.85%. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera. It can also be used as a surveillance system in airports, bus stations, etc., to reduce the risk of possible criminal threats.