Abstract
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.
Highlights
Facial recognition technology will change society in many ways
To evaluate the performance of the proposed Shannon Similarity Measure (SHS) and Renyi Similarity Measure (RSM) against structural similarity index measure (SSIM), feature similarity index measure (FSIM), ZSIM, Zernike-Minkowski Similarity (ZMSIM), and the state-of-the-art feature-based structural measure (FSM), we have to describe the experimental procedure in detail
AT&T and FEI are used to test the performance of all the measures in terms of face recognition, and TID2008 and image and video-communication (IVC) are used to test the performance of all the measures in terms of image similarity in the figures listed below
Summary
Facial recognition technology will change society in many ways. Face recognition system is available nowadays. Face recognition has been used in many commercial and law enforcement applications [1] such as some of the airport’s systems, and ATM and electronic payment started to test facial recognition in real events. This availability of efficient face recognition algorithms leads to the fact that it can be used in real-time security issues where there is nowhere to hide. Face recognition technology helps to verify the ID of the ridehailing driver, verify the permits of tourists to enter tourist places, and let people buy things with a smile [2]
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