A Fast Iterative Pursuit Algorithm in Robust Face Recognition Based on Sparse Representation

Zhao, Jian; Huang, Lizhen; Jia, Jian; Xie, Yu

doi:10.1155/2014/683494

Cited by 4 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sparse representation is one core issue of the modern theory of compressed sensing, and it is intended to represent a sample using a minimal number of nonzero coefficient terms. A number of works have shown that sparse representation and classification (SRC) algorithm can obtain a very high accuracy for image classification such as object recognition and image resolution [22][23][24][25]. However, the conventional SRC algorithm such as the one proposed in [22] has a relatively high computational cost, the improved sparse representation algorithm for fastener recognition is presented in this subsection, and our algorithm is mathematically tractable and computationally efficient.…”

Section: The Improved Sparse Representation Algorithm For Symmetricalmentioning

confidence: 99%

“…A number of works have shown that sparse representation and classification (SRC) algorithm can obtain a very high accuracy for image classification such as object recognition and image resolution [22][23][24][25]. However, the conventional SRC algorithm such as the one proposed in [22] has a relatively high computational cost, the improved sparse representation algorithm for fastener recognition is presented in this subsection, and our algorithm is mathematically tractable and computationally efficient. The first step in our algorithm aims at identifying and discarding the training samples that are "far" from the test sample and the second step then exploits the remaining training samples 1 , .…”

Section: The Improved Sparse Representation Algorithm For Symmetricalmentioning

confidence: 99%

See 1 more Smart Citation

Integrating the Symmetry Image and Improved Sparse Representation for Railway Fastener Classification and Defect Recognition

Xiong

et al. 2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The detection of fastener defects is an important task for ensuring the safety of railway traffic. The earlier automatic inspection systems based on computer vision can detect effectively the completely missing fasteners, but they have weaker ability to recognize the partially worn ones. In this paper, we propose a method for detecting both partly worn and completely missing fasteners, the proposed algorithm exploits the first and second symmetry sample of original testing fastener image and integrates them for improved representation-based fastener recognition. This scheme is simple and computationally efficient. The underlying rationales of the scheme are as follows: First, the new virtual symmetrical images really reflect some possible appearance of the fastener; then the integration of two judgments of the symmetrical sample for fastener recognition can somewhat overcome the misclassification problem. Second, the improved sparse representation method discarding the training samples that are “far” from the test sample and uses a small number of samples that are “near” to the test sample to represent the test sample, so as to perform classification and it is able to reduce the side-effect of the error identification problem of the original fastener image. The experimental results show that the proposed method outperforms state-of-the-art fastener recognition methods.

show abstract

Section: The Improved Sparse Representation Algorithm For Symmetricalmentioning

confidence: 99%

Section: The Improved Sparse Representation Algorithm For Symmetricalmentioning

confidence: 99%

Integrating the Symmetry Image and Improved Sparse Representation for Railway Fastener Classification and Defect Recognition

Xiong

et al. 2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Their proposal optimizes the computation of residual values with significant gain in computational efficiency and no considerable losses in recognition accuracy. Jian et al [ 26 ] also center their attention on the computational speed limitations of the SRC approach. Their proposal, based on the orthogonal matching pursuit (OMP) algorithm, achieves fast and robust face recognition, even though the best results are only achieved through a preliminary occlusion detection block.…”

Section: Related Workmentioning

confidence: 99%

A Cognitively-Motivated Framework for Partial Face Recognition in Unconstrained Scenarios

Monteiro

Cardoso

2015

Sensors

View full text Add to dashboard Cite

Humans perform and rely on face recognition routinely and effortlessly throughout their daily lives. Multiple works in recent years have sought to replicate this process in a robust and automatic way. However, it is known that the performance of face recognition algorithms is severely compromised in non-ideal image acquisition scenarios. In an attempt to deal with conditions, such as occlusion and heterogeneous illumination, we propose a new approach motivated by the global precedent hypothesis of the human brain's cognitive mechanisms of perception. An automatic modeling of SIFT keypoint descriptors using a Gaussian mixture model (GMM)-based universal background model method is proposed. A decision is, then, made in an innovative hierarchical sense, with holistic information gaining precedence over a more detailed local analysis. The algorithm was tested on the ORL, ARand Extended Yale B Face databases and presented state-of-the-art performance for a variety of experimental setups.

show abstract

“…Sparse representation (SR) theory [1,2] indicates that a signal can be represented by certain linear combination of a few atoms of a prespecified dictionary. It is an evolving field, with state-of-the-art results in many signal processing tasks, such as coding, denoising, face recognition, deblurring, and compressed sensing [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Note that the equivalent expression to (5) where the constraint is a fixed sparse representation error (SRE) level can also be formulated aŝ…”

Section: Introductionmentioning

confidence: 99%

An Efficient Algorithm for Learning Dictionary under Coherence Constraint

Bai

Jiang

2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Dictionary learning problem has become an active topic for decades. Most existing learning methods train the dictionary to adapt to a particular class of signals. But as the number of the dictionary atoms is increased to represent the signals much more sparsely, the coherence between the atoms becomes higher. According to the greedy and compressed sensing theories, this goes against the implementation of sparse coding. In this paper, a novel approach is proposed to learn the dictionary that minimizes the sparse representation error according to the training signals with the coherence taken into consideration. The coherence is constrained by making the Gram matrix of the desired dictionary approximate to an identity matrix of proper dimension. The method for handling the proposed model is mainly based on the alternating minimization procedure and, in each step, the closed-form solution is derived. A series of experiments on synthetic data and audio signals is executed to demonstrate the promising performance of the learnt incoherent dictionary and the superiority of the learning method to the existing ones.

show abstract

A Fast Iterative Pursuit Algorithm in Robust Face Recognition Based on Sparse Representation

Cited by 4 publications

References 15 publications

Integrating the Symmetry Image and Improved Sparse Representation for Railway Fastener Classification and Defect Recognition

Integrating the Symmetry Image and Improved Sparse Representation for Railway Fastener Classification and Defect Recognition

A Cognitively-Motivated Framework for Partial Face Recognition in Unconstrained Scenarios

An Efficient Algorithm for Learning Dictionary under Coherence Constraint

Contact Info

Product

Resources

About