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An Entropy-Histogram Approach for Image Similarity and Face Recognition
Joint Authors
Aljanabi, Mohammed Abdulameer
Lu, Song Feng
Hussain, Zahir M.
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-07-09
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
Image similarity and image recognition are modern and rapidly growing technologies because of their wide use in the field of digital image processing.
It is possible to recognize the face image of a specific person by finding the similarity between the images of the same person face and this is what we will address in detail in this paper.
In this paper, we designed two new measures for image similarity and image recognition simultaneously.
The proposed measures are based mainly on a combination of information theory and joint histogram.
Information theory has a high capability to predict the relationship between image intensity values.
The joint histogram is based mainly on selecting a set of local pixel features to construct a multidimensional histogram.
The proposed approach incorporates the concepts of entropy and a modified 1D version of the 2D joint histogram of the two images under test.
Two entropy measures were considered, Shannon and Renyi, giving a rise to two joint histogram-based, information-theoretic similarity measures: SHS and RSM.
The proposed methods have been tested against powerful Zernike-moments approach with Euclidean and Minkowski distance metrics for image recognition and well-known statistical approaches for image similarity such as structural similarity index measure (SSIM), feature similarity index measure (FSIM) and feature-based structural measure (FSM).
A comparison with a recent information-theoretic measure (ISSIM) has also been considered.
A measure of recognition confidence is introduced in this work based on similarity distance between the best match and the second-best match in the face database during the face recognition process.
Simulation results using AT&T and FEI face databases show that the proposed approaches outperform existing image recognition methods in terms of recognition confidence.
TID2008 and IVC image databases show that SHS and RSM outperform existing similarity methods in terms of similarity confidence.
American Psychological Association (APA)
Aljanabi, Mohammed Abdulameer& Hussain, Zahir M.& Lu, Song Feng. 2018. An Entropy-Histogram Approach for Image Similarity and Face Recognition. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-18.
https://search.emarefa.net/detail/BIM-1209823
Modern Language Association (MLA)
Aljanabi, Mohammed Abdulameer…[et al.]. An Entropy-Histogram Approach for Image Similarity and Face Recognition. Mathematical Problems in Engineering No. 2018 (2018), pp.1-18.
https://search.emarefa.net/detail/BIM-1209823
American Medical Association (AMA)
Aljanabi, Mohammed Abdulameer& Hussain, Zahir M.& Lu, Song Feng. An Entropy-Histogram Approach for Image Similarity and Face Recognition. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-18.
https://search.emarefa.net/detail/BIM-1209823
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1209823