GOLD Features with Machine Learning for Face Recognition

6 Pages Posted: 27 Feb 2018

See all articles by A Vinay

A Vinay

PES University

D L Akshaykanth

PES University

Anusha Kamath

PES University

Aishwarya Manjunath

PES University

K N Balasubramanya Murthy

PES University

S Natarajan

PES University

Date Written: November 15, 2017

Abstract

Face detection and recognition is a technology in which machines intelligently recognize a human from his face. Face recognition is widely used for many applications such as tracking attendance, fraud detection for passport, in banks, Facebook’s Deep face for automatic tagging of people in photos etc. The initial steps in face recognition is feature extraction. Most of the commonly used feature extraction techniques are SIFT, SURF, Linear Discriminant Analysis. In this paper we have experimented with the use of Gaussian of local descriptors (GOLD) for face recognition. An optimal size of 8384 GOLD descriptors are obtained for every image. Classification models such as Naïve Bayes, K nearest neighbor, Regression trees and multi-class ECOC are used to train with the descriptors and test the same. The dataset is partitioned in 70:30 ratio for every subject for training and testing respectively. We have used the ORL, Faces-95 and Grimace dataset to test our experimentation. From this, it was found that K nearest neighbor outperforms the others with the recall and precision of 99%.

Keywords: Face Recognition; Gaussian of local descriptors (GOLD); Naive Bayes: K nearest neighbor; Regression trees; Multi-class ECOC (Error correcting output code).

Suggested Citation

Vinay, A and Akshaykanth, D L and Kamath, Anusha and Manjunath, Aishwarya and Balasubramanya Murthy, K N and Natarajan, S, GOLD Features with Machine Learning for Face Recognition (November 15, 2017). Proceedings of the International Conference on Intelligent Computing Systems (ICICS 2017 – Dec 15th - 16th 2017) organized by Sona College of Technology, Salem, Tamilnadu, India, Available at SSRN: https://ssrn.com/abstract=3126730 or http://dx.doi.org/10.2139/ssrn.3126730

A Vinay (Contact Author)

PES University ( email )

100 Feet Ring Road
Banashankari
Bengaluru, Karnataka 560085
India

D L Akshaykanth

PES University ( email )

100 Feet Ring Road
Banashankari
Bengaluru, Karnataka 560085
India

Anusha Kamath

PES University ( email )

100 Feet Ring Road
Banashankari
Bengaluru, Karnataka 560085
India

Aishwarya Manjunath

PES University ( email )

100 Feet Ring Road
Banashankari
Bengaluru, Karnataka 560085
India

K N Balasubramanya Murthy

PES University ( email )

100 Feet Ring Road
Banashankari
Bengaluru, Karnataka 560085
India

S Natarajan

PES University ( email )

100 Feet Ring Road
Banashankari
Bengaluru, Karnataka 560085
India

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