Local Binary Patterns and Its Application to Facial Image Analysis: A Survey

Abstract

International audienceLocal Binary Patterns (LBP) is a non-parametric descriptor whose aim is to efficiently summarize the local structures of images. In recent years, it has aroused increasing interest in many areas of image processing and computer vision, and has shown its effectiveness in a number of applications, in particular for facial image analysis, including tasks as diverse as face detection, face recognition, facial expression analysis, demographic classification, etc. This paper presents a comprehensive survey of LBP metho-dology including several more recent variations. As a typical ap-plication of the LBP approach, LBP-based facial image analysis is extensively reviewed, while its successful extensions in dealing with various tasks of facial image analysis are also highlighted

Survei Penelitian Pengenalan Pola dalam Identifikasi Biometrik

“…Gambar 8 Pemilihan Parameter P dan R pada LBP [28] Proses operator LBP di atas merupakan operator LBP yang pertama kali dikembangkan oleh Ojala et al, hingga saat ini banyak sekali penelitian pengembangan operator LBP. Pengembangan ini dikhususkan untuk meningkatkan kemampuan membedakan [29][30], meningkatkan ketahanan terhadap perubahan (robustness) [31][32], pengembangan dalam penelitian pemilihan neighbourhood [33][34], pengembangkan dalam ranah dimensi tiga [35][36], pengembangan dalam kombinasi penggunaan dengan metode lain [37][38].…”

Performance Study of Soft Local Binary Pattern over Local Binary Pattern under Noisy Images

“…The center value is constructed by concatenating the binary numbers from top left corner in clockwise direction. And finally the decimal value is produced by multiplying the threshold values with weights given to the corresponding pixels and summing up the result, is called LBP codes [15] as shown in Figure 2. where i c and i P are, respectively, gray-level values of the central pixel and P surrounding pixels in the circle neighborhood with a radius R of given pixel at (x c , y c ), and function s(x) is defined as [14].…”

“…But in soft histogram version, one pixel typically contributes to more than one bin [15]- [16]. Soft Local Binary Pattern is more efficient than LBP in case of noisy images [15]- [16].…”

Real-time Intelligent Alert System on Driver�s Hypo-Vigilance Detection Using Template Matching Technique

“…Like [24], the Gaussian distribution of skin colors in the RGB color model is used to locate a face region. Then, the face is binarized with a fixed threshold to locate the eyes' positions with some geometric restrictions and to track the eyes by using the Hausdorff distance template matching.…”