Weighted statistical binary patterns for facial feature representation

Hung Phuoc Truong,Thanh Phuong Nguyen,Yong-Guk Kim

doi:10.1007/s10489-021-02477-1

Hung Phuoc Truong, Thanh Phuong Nguyen + Show 1 more

Open Access

https://doi.org/10.1007/s10489-021-02477-1

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Abstract

We present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.

Highlights

Artificial intelligence has been developing rapidly with many real-world applications such as time series prediction [24], image classification [40, 46], and smart cities [28]
– To extract more robust descriptors from salient information in statistical moments, we propose the fused histogram of Complementary Local Binary Patterns by direction α (CLBPα), that is constructed by using Weighted Statistical Binary Patterns by direction α (WSBPα) to obtain enriched features
73.45 90.68 94.48 96.61 97.22 97.27 69.42 90.05 94.66 96.93 97.57 97.84 using many parameters (P, R) because it could lead to a high dimensional descriptor. – Evaluation using six face datasets suggests that our descriptors outperform state-of-the-art methods, such as EL-Local Binary Pattern (LBP) [44], AECLBP-S (B16) [22], Multi-resolution dictionary [30], DR-LBP + LDA [35], Local Diagonal Extrema Number Pattern (LDENP) [42]

Summary

Introduction

Artificial intelligence has been developing rapidly with many real-world applications such as time series prediction [24], image classification [40, 46], and smart cities [28]. The face image can be obtained from the camera as a non-invasive acquisition process. Face recognition can be widely applied to public. A face recognition application typically consists of face detection, feature extraction, and classification. Most wellknown methods have robust feature descriptors, highly discriminative, and robust to extrinsic changes. Most face recognition algorithms, which have been studied extensively in addressing robust and discriminative descriptors, focus on three primary techniques: holistic, local, and hybrid models [23]. The local approach considers certain facial features

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Discussion

Conclusion