Paper Info
Title
Face Identification using Local Ternary Tree Pattern based Spatial Structural Components.
Abstract
This paper reports groundbreaking results of a face identification system which makes use of a novel local descriptor called Local Ternary Tree Pattern. Devising deft and feasible local descriptors for a face image plays an emergent preface in face identification task when the system performs in presence of lots of variety of face images including constrained, unconstrained and plastic surgery images. The LTTP has been proposed to extract robust and discriminatory spatial features from a face image as this descriptor can be used to best describe the various structural components of a face. To extract the most useful features, a ternary tree is formed for each pixel with its eight neighbors. LTTP pattern can be generated in four ways: LTTP Left Depth, LTTP Left Breadth, LTTP Right Depth and LTTP Right Breadth. The encoding schemes of these four patterns generation are very simple and efficient in terms of computational complexity as well as time complexity. The proposed face identification system is tested on six face databases, namely, the UMIST, the JAFFE, the extended Yale face B, the Plastic Surgery, the LFW and the UFI. The experimental evaluation demonstrates the most outstanding results which will have long term impact in designing face identification systems considering a variety of faces captured under different environments.
Year
DOI
Venue
2019
10.1007/978-3-030-31321-0_5
arXiv: Computer Vision and Pattern Recognition
DocType
Volume
Citations 
Journal
abs/1905.00693
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Rinku Datta Rakshit100.34
Dakshina Ranjan Kisku211916.95
Massimo Tistarelli393981.95
Phalguni Gupta480582.58