Face Identification using Local Ternary Tree Pattern based Spatial Structural Components

TL;DR

This paper introduces a novel Local Ternary Tree Pattern (LTTP) descriptor for face identification, demonstrating robust performance across various face databases and challenging conditions.

Contribution

The paper proposes the LTTP descriptor, a new local feature extraction method using ternary tree patterns for improved face identification accuracy.

Findings

Achieved promising results on six diverse face databases.

Compared favorably with existing local descriptors.

Effective in unconstrained and plastic surgery face images.

Abstract

This paper reports a face identification system which makes use of a novel local descriptor called Local Ternary Tree Pattern (LTTP). Exploiting and extracting distinctive local descriptor from a face image plays a crucial role in face identification task in the presence of a variety of face images including constrained, unconstrained and plastic surgery images. LTTP has been used to extract robust and useful spatial features which use to describe the various structural components on a face. To extract the features, a ternary tree is formed for each pixel with its eight neighbors in each block. LTTP pattern can be generated in four forms such as LTTP Left Depth (LTTP LD), LTTP Left Breadth (LTTP LB), LTTP Right Depth (LTTP RD) and LTTP Right Breadth (LTTP RB). The encoding schemes of these patterns are very simple and efficient in terms of computational 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 promising results considering a variety of faces captured under different environments. The proposed LTTP based system is also compared with some local descriptors under identical conditions.