Comparison of HOG and fisherfaces based face recognition system using MATLAB

Title

Comparison of HOG and fisherfaces based face recognition system using MATLAB

Creator

Megha Chandra Reddy N.V.; Kumar K.

Description

Face recognition and validation is not an easy task due to barriers in between like variation in pose, facial expressions and illumination. There are many algorithms available to build a face recognition system. One such popular method of approach is the Histogram of Oriented Gradients (HOG). It is a simple but effective algorithm. Even though it gives satisfactory results, it sometimes mismatches query image with irrelevant images, especially in poor lighting conditions. This paper presents a more accurate technique called Fisherfaces. It is a more reliable method for face recognition and validation. Fisherface algorithm is utilized primarily for reducing the dimensionality of the feature space. Fisherface method for image recognition involves a series of steps. Firstly, the face space dimension is reduced using Principal Component Analysis (PCA) method, then the Linear Discriminant Analysis (LDA) method is used for feature extraction. Fisherface method produced good results even under complex situations like varying illumination conditions and images with different poses and expressions which is a major drawback of HOG. Fisherface algorithm can reach a maximum accuracy of 96.87%. Error Correcting Output Code (ECOC) is the classifier used for both HOG and Fisherfaces. 2021 IEEE.

Source

2021 2nd International Conference for Emerging Technology, INCET 2021

Date

2021-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Error-correcting output code (ECOC); Fisherface; Histogram of oriented gradients (HOG); Linear discriminant analysis (LDA); Principal component analysis (PCA)

Coverage

Megha Chandra Reddy N.V., Christ(Deemed to Be) University, Dept. of Electronics and Communication Engineering, Bangalore, India; Kumar K., Christ(Deemed to Be) University, Dept. of Electronics and Communication Engineering, Bangalore, India

Rights

Restricted Access

Relation

ISBN: 978-172817029-9

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/INCET51464.2021.9456366

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