A Survey on Local Textural Patterns for Facial Feature Extraction

A Survey on Local Textural Patterns for Facial Feature Extraction

Uma Maheswari V (Vardhaman College of Engineering, Hyderabad, India), Golla Vara Prasad (BMS College of Engineering, Bangalore, India) and S Viswanadha Raju (JNTUH College of Engineering Jagtial, Telangana, India)
Copyright: © 2018 |Pages: 26
DOI: 10.4018/IJCVIP.2018040101
OnDemand PDF Download:
No Current Special Offers


Over the last two decades retrieving an accurate image has become a challenging task. Regardless, texture patterns address this problem by decreasing the significant gap between the actual image over the user expectation rather than other low-level features. This article represents the comprehensive survey of the recent achievements and relevant publications investigated in different directions of the textural areas in CBIR. These consist of triggered methods for image local texture feature extraction, numerical illustration and similarity measurement. In addition, challenges are discussed in comparisons of textural patterns. Retrospectively, concluded with a few recommendations based on generic survey and demand from the
Article Preview

1. Introduction

The-state-of-art escalation in the digital data production steadily due to the dominant use of internet and digital equipment in various fields such as medical (Mitra, Murthy, & Pal, 2004; Zhang, Brady, & Smith, 2001), entertainment, education, media, online business etc. becoming by keywords. This makes the system cumbersome to manage the abundant data is and human annotations as if in text-based systems. Therefore, here is a tremendous call for an efficient system to retrieve the precise images from the massive database rather than labels. Today Content based image retrieval (CBIR) system is the most well-known system for some applications, CBIR consists the important and essential steps such as feature extraction, relevance feedback, similarity measurements etc., and here feature extraction is the most prominent step in preprocessing that depends on the technique make use of extract the features from the only image like local data as color, texture, shape, human faces etc., further features are categorized into local features such as color features, layout, shape features and texture (Deng, Manjunath, Kenney, Moore, & Shin, 2001; Manjunath, Ohm, Vasudevan, & Yamada, 2001) and high-level features such as faces, biometric, neural networks etc. To retrieve an accurate image with the help of one and only feature is arduous due to the probability of user taking photographs in any direction like several regions which include random direction of capturing of image, optical device, uneven illumination and that of posing expressions, relevance feedback etc. (Jing, Li, Zhang, & Zhang, 2004; Su, Zhang, Li & Ma, 2003) so the system demands the combination of two or multiple features and filtering process. A general and upgraded survey is typified in the further sections.

Complete Article List

Search this Journal:
Volume 12: 4 Issues (2022): 1 Released, 3 Forthcoming
Volume 11: 4 Issues (2021)
Volume 10: 4 Issues (2020)
Volume 9: 4 Issues (2019)
Volume 8: 4 Issues (2018)
Volume 7: 4 Issues (2017)
Volume 6: 2 Issues (2016)
Volume 5: 2 Issues (2015)
Volume 4: 2 Issues (2014)
Volume 3: 4 Issues (2013)
Volume 2: 4 Issues (2012)
Volume 1: 4 Issues (2011)
View Complete Journal Contents Listing