A Feature-Based Structural Measure: An Image Similarity Measure for Face Recognition

Noor Shnain,Zahir Hussain,Song Lu

doi:10.3390/app7080786

Abstract

Facial recognition is one of the most challenging and interesting problems within the field of computer vision and pattern recognition. During the last few years, it has gained special attention due to its importance in relation to current issues such as security, surveillance systems and forensics analysis. Despite this high level of attention to facial recognition, the success is still limited by certain conditions; there is no method which gives reliable results in all situations. In this paper, we propose an efficient similarity index that resolves the shortcomings of the existing measures of feature and structural similarity. This measure, called the Feature-Based Structural Measure (FSM), combines the best features of the well-known SSIM (structural similarity index measure) and FSIM (feature similarity index measure) approaches, striking a balance between performance for similar and dissimilar images of human faces. In addition to the statistical structural properties provided by SSIM, edge detection is incorporated in FSM as a distinctive structural feature. Its performance is tested for a wide range of PSNR (peak signal-to-noise ratio), using ORL (Olivetti Research Laboratory, now AT&T Laboratory Cambridge) and FEI (Faculty of Industrial Engineering, São Bernardo do Campo, São Paulo, Brazil) databases. The proposed measure is tested under conditions of Gaussian noise; simulation results show that the proposed FSM outperforms the well-known SSIM and FSIM approaches in its efficiency of similarity detection and recognition of human faces.

Highlights

In view of the current issues of terrorism, security in the western world has been heightened, and is becoming an important and challenging task
The proposed measure (FSM) gives a reliable similarity between any two images even under noise, in other words, Feature-Based Structural Measure (FSM) produces maximal similarity when the images are similar, while giving near-zero similarity when the images are dissimilar. These properties gave the proposed measure high ability to recognize face images under noisy conditions, different facial expressions and pose variations. Such properties are highly needed in security applications while checking the identity of a specific face image in a big database
Most automatic face recognition systems are dependent on a comparison between a given face image and images saved in memory

Summary

Introduction

In view of the current issues of terrorism, security in the western world has been heightened, and is becoming an important and challenging task. In other words, existing systems still fall far short of the abilities of a human perception system This challenge is mainly due to factors that affect the features of an image, such as changes in illumination, background, facial expression. During the last few years, many studies, methods and approaches to similarity and recognition of human faces have been presented to achieve high success rates for accuracy, confidence in identification and authentication in security systems. Wang and Bovik in 2004 [9], when they proposed the SSIM This measure is based on the statistical similarity between two images. Proposed a similarity measure based on Hausdorff distance for face recognition. This measure can provide similarity and dissimilarity information of two objects to compare them such as faces with different illumination condition and facial expression. The measure has a better performance than the measures based on conventional Hausdorff distance and the eigenface approaches [11]

Objectives

Results

Discussion

Conclusion