Palmprint identification using sparse and dense hybrid representation

Al-Maadeed, Somaya; Jiang, Xudong; Rida, Imad; Bouridane, Ahmed

doi:10.1007/s11042-018-5655-8

Cited by 22 publications

(11 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To meet this challenging goal, supervised dictionary learning approach has been shown to be an efficient means to lead state-of-the-art results in many applications including signal classification [21]. Indeed, in recent years there has been a growing interest in the use of techniques such as sparse representation for classification (SRC) or sparse and dense representation (SDR) in order to build discriminative representations by minimizing the intra-class homogeneity, maximizing class separability and promoting sparsity for more generalization ability [22], [23], [24]. This is done by learning a dictionary per class and making them dissimilar by boosting the pairwise orthogonality.…”

Section: Introductionmentioning

confidence: 99%

A pBCI to Predict Attentional Error Before it Happens in Real Flight Conditions

Dehais

Rida

Roy

et al. 2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

Self Cite

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

A pBCI to Predict Attentional Error Before it Happens in Real Flight Conditions

Dehais

Rida

Roy

et al. 2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

Self Cite

View full text Add to dashboard Cite

“…Fourier [16], Cosine [5,17], Wavelet [15,18] and Radon transforms [19] are common transforms that are applied to cope with these degradations. Recently, sparse representation [20][21][22][23] is used to enhance accuracy. To overcome the problems of the quality and the limited number of training data, Maadeed et al [22] exploit sparse-anddense hybrid representation.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, sparse representation [20][21][22][23] is used to enhance accuracy. To overcome the problems of the quality and the limited number of training data, Maadeed et al [22] exploit sparse-anddense hybrid representation. In [23], the authors firstly perform an ensemble learning based on random subspace sampling over 2D-PCA space.…”

Section: Related Workmentioning

confidence: 99%

Masked SIFT with align‐based refinement for contactless palmprint recognition

et al. 2018

View full text Add to dashboard Cite

Contactless palmprint is considered more user convenient than other biometrics due to its acquisition simplicity and less-private nature. Many challenges arise which affect the performance of common contact-based methods when applied to a contactless environment. For example, pose and illumination variations affect the layout and visibility of palm lines. This study proposes a SIFT-based method with three main modifications from the traditional SIFT. First, the palm regions with no significant lines/wrinkles are masked out to reduce the false features. A region with multi-lines is then described by multi-descriptors rather than a single one. Second, only query and target keypoints with small rotation difference are compared together, instead of comparing them all. This speed-up the comparison and enhance the accuracy, versus SIFT, by reducing the wrong matches. Third, an align-based refinement is applied to filter out the incorrect matches. The method is tested on three contactless hand databases; IITD, GPDS and Sfax-Miracl. It achieves a verification equal error rate of 0.72, 0.84 and 1.14% and a correct identification rate of 98.9, 99 and 98.9% on each database, respectively. These results are significantly better than the state-ofart methods on the same databases by 1.9% for verification and 3.2% for identification.

show abstract

“…It is a non-linear feature subspace research method. The essence of image recognition with it is to seek a low-dimensional image manifold in the image space where the image is located [11][12][13]. For the non-linear description of image space, more representative algorithms are equidistant mapping algorithm, local linear embedding, Laplace feature mapping, etc [14,15].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Recognition Simulation of 3D Multimedia Visual Image Based on Sparse Representation Algorithm

Chen

2020

IEEE Access

View full text Add to dashboard Cite

With the rapid development of computer networks and multimedia technologies, images, which are important carriers of information dissemination, have made human cognition of things easier. Image recognition is a basic research task in computer vision, multimedia search, image understanding and other fields. This paper proposes a hierarchical feature learning structure that is completely automatically based on the original pixels of the image, and uses the K-SVD (K-Singular Value Decomposition) algorithm with label consistency constraints to train the discriminant dictionary. For different types of image data sets, the algorithm only extracts image blocks. After dense sampling, an efficient OMP (Orthogonal Matching Pursuit) encoder is used to obtain a layered sparse representation. The improved SIFT (Scale Invariant Feature Transform) algorithm is used to solve the difficult problem of multimedia visual image stereo matching. The feature point extraction and stereo matching of multimedia visual images, different scales and different viewpoint images are analyzed separately. Aiming at a large number of low-dimensional geometric features of 3D images, this paper studies the extraction and sorting strategies of low-dimensional geometric features of 3D images. A sparse representation method for 3D images is proposed, and the sparseness of image features is evaluated. This further improves the accuracy of 3D image representation and the robustness of 3D image recognition algorithms.

show abstract

Palmprint identification using sparse and dense hybrid representation

Cited by 22 publications

References 57 publications

A pBCI to Predict Attentional Error Before it Happens in Real Flight Conditions

A pBCI to Predict Attentional Error Before it Happens in Real Flight Conditions

Masked SIFT with align‐based refinement for contactless palmprint recognition

Artificial Intelligence Recognition Simulation of 3D Multimedia Visual Image Based on Sparse Representation Algorithm

Contact Info

Product

Resources

About