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Project Title

Brief introduction to this project.
exp: This project implements many recently face recognition algorithms based on statistical learning, including LRC^[1], RRC, SRC^[2], CRC^[3], Euler RRC, Euler SRC^[4], and Euler CRC.********

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

What things you need to install the software and how to install them.

Installing

A step by step series of examples that tell you how to get a development env running.
Say what the step will be.

Running code

Running the test

Run the test.py, and you will get output as follows if success

>> The accuracy of LRC on AR datest is: 0.7271.

Examples

import numpy as np

from dataset import AR
from model.subspace_regression import LRC

train_xs, train_ys, test_xs, test_ys = AR.exp1(mode=2)
model = LRC()
model.fit(train_xs, train_ys)
acc = model.score(test_xs, test_ys)
print(f'The accuracy of {model.__class__.__name__} on AR datest is: {acc}.\n')

# Output
>> The accuracy of LRC on AR datest is: 0.7271.

Authors

• JsHou

License

This project is licensed under the MIT License.

Acknowledge

• Thanks to the authors of the papers cited in this project.

References

[1] Naseem I, Togneri R, Bennamoun M. Linear regression for face recognition[J]. TPAMI, 2010;32(11):2106-12.

[2] Wright J, Yang AY, Ganesh A, et al. Robust face recognition via sparse representation[J]. TPAMI, 2008;31(2):210-27.

[3] Zhang L, Yang M, Feng X. Sparse representation or collaborative representation: Which helps face recognition?[C]. ICCV, 2011.

[4] Liu Y, Gao Q, Han J, et al. Euler sparse representation for image classification[C]. AAAI, 2018.

[5] Liwicki S, Tzimiropoulos G, Zafeiriou S, et al. Euler Principal Component Analysis[J]. IJCV, 2012;101(3):498-518.

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