Face Recognition Based on Improved LTP

Abstract

Abstract-Localized binary model (LBP) is an efficient local feature description operator. As a nonparametric description operator, it has received more and more attention and has achieved great success in the field of face recognition. In this paper, we introduce only the first-order non-directional feature of LBP operator, and introduce the high-order differential ULDP operator in four directions, and apply the preprocessing method to face recognition. In addition, the threshold for the LBP operator is completely dependent on the defects of the central pixels. In this paper, the local threshold model (ALTP) of the adaptive threshold is proposed. The threshold of the region is automatically generated by calculating the mean and variance of the local region pixels. The experimental results in several commonly used face databases show that ULDP and ALTP in this paper have good robustness to face recognition in nonconstrained environment, especially face recognition with illumination change.

Research of SWNMF with New Iteration Rules for Facial Feature Extraction and Recognition

“…Then one auxiliary function Z(v,v t ) for J(v) is constructed as Equation (9), which satisfies the conditions as Equation (11). Meanwhile, v is updated by Equation (12) [25]:…”

“…However, the dimension of the characteristics extracted by Gabor wavelet is also too high, which leads to the long operation time and ineffectiveness of the algorithm. In order to solve the problem, some local texture information operators with low dimensions are proposed to extract the facial features, such as LBP (Local Binary Pattern) [8] and LTP (Local Ternary Pattern) [9]. The method of LBP can depict different features of local image texture by binarizing the gray value of the central point pixel and its surrounding point pixel.…”