RATIONAL STRUCTURAL-ZERNIKE MEASURE: AN IMAGE SIMILARITY MEASURE FOR FACE RECOGNITION
الباحث الأول:
Noor Abd Alrazak Shnain
الباحثين الآخرين:
Zahir M Hussain, Mohammed Abdulameer Aljanabi, Song Feng Lu
المجلة:
International Journal of Innovative Research in Information Security (IJIRIS)
تاريخ النشر:
None
مختصر البحث:
Image similarity or image distortion assessment is the underlying technology in many computer vision applications, and is the root of many algorithms used in image processing. Many similarity measures have been proposed with the aim of achieving a h…
Image similarity or image distortion assessment is the underlying technology in many computer vision applications, and is the root of many algorithms used in image processing. Many similarity measures have been proposed with the aim of achieving a high level of accuracy, and each of these measures has its strength as well as its weaknesses. In this paper, we present a highly efficient hybrid measure for image similarity that is based on structural and momental measures. We propose a similarity measure called the rational structural-Zernike measure (ZSM), to determine a reliable similarity between any two images including human faces images. This measure combines the best features of two structural measures, the well-known structural similarity index measure (SSIM) and the feature similarity index for image quality assessment (FSIM), with Zernike moments (ZMs), which have proven effective in the extraction of image features. Simulation results show that the proposed measure outperforms the SSIM, FSIM, ZMs and the state-of-art measure Feature-Based Structural Measure (FSM) through its ability to detect similarity even under distortion and to recognise the similarity between images of human faces under various conditions of facial expression and pose.
