# Efficient Image Set Classification using Linear Regression based Image Reconstruction

## TL;DR

This paper introduces a fast and effective image set classification method that uses linear regression models on downsampled gallery sets, achieving high accuracy with less training data and faster computation.

## Contribution

The paper presents a novel linear regression-based approach for image set classification that avoids training and improves speed while maintaining accuracy.

## Key findings

- Achieved competitive accuracy with minimal training data.
- Demonstrated superior computational speed over existing methods.
- Validated on multiple benchmark datasets for face and object classification.

## Abstract

We propose a novel image set classification technique using linear regression models. Downsampled gallery image sets are interpreted as subspaces of a high dimensional space to avoid the computationally expensive training step. We estimate regression models for each test image using the class specific gallery subspaces. Images of the test set are then reconstructed using the regression models. Based on the minimum reconstruction error between the reconstructed and the original images, a weighted voting strategy is used to classify the test set. We performed extensive evaluation on the benchmark UCSD/Honda, CMU Mobo and YouTube Celebrity datasets for face classification, and ETH-80 dataset for object classification. The results demonstrate that by using only a small amount of training data, our technique achieved competitive classification accuracy and superior computational speed compared with the state-of-the-art methods.