Facial Biometric System for Recognition using Extended LGHP Algorithm on Raspberry Pi

TL;DR

This paper presents a facial biometric recognition system using an extended LGHP algorithm on Raspberry Pi, demonstrating improved accuracy and performance over existing descriptors on multiple challenging facial image datasets.

Contribution

The paper introduces an extended version of the Local Gradient Hexa Pattern (LGHP) algorithm and implements it on a Raspberry Pi-based biometric system, enhancing recognition accuracy and efficiency.

Findings

Extended LGHP outperforms LDP, LTrP, MLBP, and LVP on benchmark datasets.

System achieves higher accuracy on Yale-B, CMU-PIE, FERET, LFW, and Ghallager databases.

Proposed system is competitive with existing patents in biometric recognition.

Abstract

In todays world, where the need for security is paramount and biometric access control systems are gaining mass acceptance due to their increased reliability, research in this area is quite relevant. Also with the advent of IOT devices and increased community support for cheap and small computers like Raspberry Pi its convenient than ever to design a complete standalone system for any purpose. This paper proposes a Facial Biometric System built on the client-server paradigm using Raspberry Pi 3 model B running a novel local descriptor based parallel algorithm. This paper also proposes an extended version of Local Gradient Hexa Pattern with improved accuracy. The proposed extended version of LGHP improved performance as shown in performance analysis. Extended LGHP shows improvement over other state-of-the-art descriptors namely LDP, LTrP, MLBP and LVP on the most challenging benchmark facial image databases, i.e. Cropped Extended Yale-B, CMU-PIE, color-FERET, LFW, and Ghallager database. Proposed system is also compared with various patents having similar system design and intent to emphasize the difference and novelty of the system proposed.