scholarly journals Robust Face Recognition via Block Sparse Bayesian Learning

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Taiyong Li ◽  
Zhilin Zhang
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Bayesian Learning ◽  
State Of The Art ◽  
Basis Functions ◽  
Training Dataset ◽  
Sparse Bayesian Learning ◽  
Sparse Model ◽  
Test Face ◽  
Robust Face Recognition

Face recognition (FR) is an important task in pattern recognition and computer vision. Sparse representation (SR) has been demonstrated to be a powerful framework for FR. In general, an SR algorithm treats each face in a training dataset as a basis function and tries to find a sparse representation of a test face under these basis functions. The sparse representation coefficients then provide a recognition hint. Early SR algorithms are based on a basic sparse model. Recently, it has been found that algorithms based on a block sparse model can achieve better recognition rates. Based on this model, in this study, we use block sparse Bayesian learning (BSBL) to find a sparse representation of a test face for recognition. BSBL is a recently proposed framework, which has many advantages over existing block-sparse-model-based algorithms. Experimental results on the Extended Yale B, the AR, and the CMU PIE face databases show that using BSBL can achieve better recognition rates and higher robustness than state-of-the-art algorithms in most cases.

ROBUST FACE RECOGNITION BY UTILIZING COLOR INFORMATION AND SPARSE REPRESENTATION

2014 ◽  
Vol 28 (03) ◽  
pp. 1456004 ◽  
Author(s):  
BILLY YL LI ◽  
WANQUAN LIU ◽  
SENJIAN AN ◽  
ANEESH KRISHNA
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Detailed Analysis ◽  
State Of The Art ◽  
Color Space ◽  
The State ◽  
Color Information ◽  
Performance Metric ◽  
Robust Face Recognition ◽  
Facial Images

In this paper, we consider the problem of robust face recognition using color information. In this context, sparse representation-based algorithms are the state-of-the-art solutions for gray facial images. We will integrate the existing sparse representation-based algorithms with color information and this integration can improve the previous performances significantly. Furthermore, we propose a new performance metric, namely the discriminativeness (DIS) to describe the recognition effectiveness for sparse representation algorithms. We find out that the richer information in color space can be used to increase the DIS, i.e. enhancing the robustness in face recognition. Extensive experiments have been conducted under different conditions, including various feature extractors, random pixel corruptions and occlusions on AR and GT databases, to demonstrate the advantages of using color information in robust face recognition. Detailed analysis is also included for each experiment to explain why and how color improve the robustness of different sparse representation-based methods.

scholarly journals A Novel STAP Algorithm for Airborne MIMO Radar Based on Temporally Correlated Multiple Sparse Bayesian Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hanwei Liu ◽  
Yongshun Zhang ◽  
Yiduo Guo ◽  
Qiang Wang ◽  
Yifeng Wu
Keyword(s):  
Sparse Representation ◽  
Bayesian Learning ◽  
Multiple Input Multiple Output ◽  
Time Spectrum ◽  
Weight Vector ◽  
Mimo Radar ◽  
Sparse Bayesian Learning ◽  
Multiple Measurement Vectors ◽  
Input Multiple Output ◽  
Temporally Correlated

In a heterogeneous environment, to efficiently suppress clutter with only one snapshot, a novel STAP algorithm for multiple-input multiple-output (MIMO) radar based on sparse representation, referred to as MIMOSR-STAP in this paper, is presented. By exploiting the waveform diversity of MIMO radar, each snapshot at the tested range cell can be transformed into multisnapshots for the phased array radar, which can estimate the high-resolution space-time spectrum by using multiple measurement vectors (MMV) technique. The proposed approach is effective in estimating the spectrum by utilizing Temporally Correlated Multiple Sparse Bayesian Learning (TMSBL). In the sequel, the clutter covariance matrix (CCM) and the corresponding adaptive weight vector can be efficiently obtained. MIMOSR-STAP enjoys high accuracy and robustness so that it can achieve better performance of output signal-to-clutter-plus-noise ratio (SCNR) and minimum detectable velocity (MDV) than the single measurement vector sparse representation methods in the literature. Thus, MIMOSR-STAP can deal with badly inhomogeneous clutter scenario more effectively, especially suitable for insufficient independent and identically distributed (IID) samples environment.

Learning a Sparse Representation for Robust Face Recognition

2013 ◽  
pp. 233-240
Author(s):  
Weihua Ou ◽  
Xinge You ◽  
Pengyue Zhang ◽  
Xiubao Jiang ◽  
Ziqi Zhu ◽  
...  
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Robust Face Recognition

scholarly journals Efficient Detection of Occlusion prior to Robust Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Rui Min ◽  
Abdenour Hadid ◽  
Jean-Luc Dugelay
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Video Surveillance ◽  
Real World ◽  
State Of The Art ◽  
Efficient Detection ◽  
Art Works ◽  
Enormous Amount ◽  
Recognition Systems ◽  
Robust Face Recognition

While there has been an enormous amount of research on face recognition under pose/illumination/expression changes and image degradations, problems caused by occlusions attracted relatively less attention. Facial occlusions, due, for example, to sunglasses, hat/cap, scarf, and beard, can significantly deteriorate performances of face recognition systems in uncontrolled environments such as video surveillance. The goal of this paper is to explore face recognition in the presence of partial occlusions, with emphasis on real-world scenarios (e.g., sunglasses and scarf). In this paper, we propose an efficient approach which consists of first analysing the presence of potential occlusion on a face and then conducting face recognition on the nonoccluded facial regions based on selective local Gabor binary patterns. Experiments demonstrate that the proposed method outperforms the state-of-the-art works including KLD-LGBPHS, S-LNMF, OA-LBP, and RSC. Furthermore, performances of the proposed approach are evaluated under illumination and extreme facial expression changes provide also significant results.

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