New Robust Part-Based Model with Affine Transformations for Facial Landmark Localization and Detection in Big Data

Chentao Zhang,Jielong Guo,Peidong Liang,Habte Tadesse Likassa,Angelos Markopoulos

doi:10.1155/2021/9995074

Abstract

In this paper, we developed a new robust part-based model for facial landmark localization and detection via affine transformation. In contrast to the existing works, the new algorithm incorporates affine transformations with the robust regression to tackle the potential effects of outliers and heavy sparse noises, occlusions and illuminations. As such, the distorted or misaligned objects can be rectified by affine transformations and the patterns of occlusions and outliers can be explicitly separated from the true underlying objects in big data. Moreover, the search of the optimal parameters and affine transformations is cast as a constrained optimization programming. To mitigate the computations, a new set of equations is derived to update the parameters involved and the affine transformations iteratively in a round-robin manner. Our way to update the parameters compared to the state of the art of the works is relatively better, as we employ a fast alternating direction method for multiplier (ADMM) algorithm that solves the parameters separately. Simulations show that the proposed method outperforms the state-of-the-art works on facial landmark localization and detection on the COFW, HELEN, and LFPW datasets.

Highlights

Localization of detailed facial features arises in a variety of applications including occlusion coherence for prediction in high-dimensional images [1], landmark localization [2,3,4], head pose estimation [5, 6], face image alignment [7], lowrank estimation [8,9,10], and object detection [11,12,13,14], etc
Conducted simulations show that the proposed method outperforms the state-of-the-art works on face detection and landmark localization on some common on the Caltech Occluded Faces in the Wild (COFW), HELEN, and Labeled Face Parts in the Wild (LFPW) datasets
Three datasets are considered in the simulations, including the Labeled Face Parts in the Wild (LFPW) [65], the HELEN68 [21], and the more challenging Caltech Occluded Faces in the Wild (COFW) [66] datasets

Summary

Introduction

Localization of detailed facial features arises in a variety of applications including occlusion coherence for prediction in high-dimensional images [1], landmark localization [2,3,4], head pose estimation [5, 6], face image alignment [7], lowrank estimation [8,9,10], and object detection [11,12,13,14], etc. To tackle the misalignment problem, [8, 9, 32,33,34] addressed several algorithms via affine transformation and the L2,1 norms To circumvent this dilemma, Martinez et al [35] considered a robust facial landmark detection (RFLD) based on high-dimensional image data via L2,1 norm regularization against poor initializations. To boost the performance of the algorithms, [38,39,40] proposed a novel deep network method for detecting facial region and landmarks to learn from lowdimensional image representations; the proposed techniques seek affine transformation to deal with occlusions and illuminations To tackle this dilemma, [8, 32, 33] proposed new methods assisted via affine transformation in high-dimensional images to estimate the optimal parameters corresponding to the low rank recovery

Methods

Results

Conclusion