Face Recognition using Compressive Sensing

TL;DR

This paper explores the application of Compressive Sensing in face recognition, demonstrating how it can recover signals from fewer samples, reducing memory and acquisition time, validated through experiments with varying sampling percentages.

Contribution

It introduces a novel application of Compressive Sensing in face recognition and verifies its effectiveness using Total Variation minimization with experimental validation.

Findings

Successful signal recovery with fewer samples

Reduced memory and acquisition time in face recognition

Effective use of Total Variation minimization

Abstract

This paper deals with the Compressive Sensing implementation in the Face Recognition problem. Compressive Sensing is new approach in signal processing with a single goal to recover signal from small set of available samples. Compressive Sensing finds its usage in many real applications as it lowers the memory demand and acquisition time, and therefore allows dealing with huge data in the fastest manner. In this paper, the undersampled signal is recovered using the algorithm based on Total Variation minimization. The theory is verified with an experimental results using different percentage of signal samples.