Fusing landmark-based features at kernel level for face recognition

Huang, Ke-Kun; Dai, Dao‐Qing; Ren, Chuan-Xian; Yu, Yu-Feng; Lai, Zhao-Rong

doi:10.1016/j.patcog.2016.10.021

Cited by 13 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning-based methods have shown excellent performance in recent years; however, they usually use not only the specified training data but also outside data. 1 By contrast, fused descriptors are also competitive [14,15], especially when there is no outside data available.…”

Section: Introductionmentioning

confidence: 99%

Selective multi-descriptor fusion for face identification

Wei

Wang

Scotney

et al. 2019

Int. J. Mach. Learn. & Cyber.

View full text Add to dashboard Cite

Over the last 2 decades, face identification has been an active field of research in computer vision. As an important class of image representation methods for face identification, fused descriptor-based methods are known to lack sufficient discriminant information, especially when compared with deep learning-based methods. This paper presents a new face representation method, multi-descriptor fusion (MDF), which represents face images through a combination of multiple descriptors, resulting in hyper-high dimensional fused descriptor features. MDF enables excellent performance in face identification, exceeding the state-of-the-art, but it comes with high memory and computational costs. As a solution to the high cost problem, this paper also presents an optimisation method, discriminant ability-based multi-descriptor selection (DAMS), to select a subset of descriptors from the set of 65 initial descriptors whilst maximising the discriminant ability. The MDF face representation, after being refined by DAMS, is named selective multi-descriptor fusion (SMDF). Compared with MDF, SMDF has much smaller feature dimension and is thus usable on an ordinary PC, but still has similar performance. Various experiments are conducted on the CAS-PEAL-R1 and LFW datasets to demonstrate the performance of the proposed methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Selective multi-descriptor fusion for face identification

Wei

Wang

Scotney

et al. 2019

Int. J. Mach. Learn. & Cyber.

View full text Add to dashboard Cite

show abstract

“…Notwithstanding the contextual paradigm sifts just described, work on the underlying fundamentals of face recognition continues with unabated effort [18], [19], [20], [21], [22], with many outstanding challenges. In recent years, particularly promising innovations are arising from the use of sparse coding [2], [21], [23], dictionary representations [24], [25], and deep learning [26], [27], [28], [17].…”

Section: Introductionmentioning

confidence: 99%

Employing Domain Specific Discriminative Information to Address Inherent Limitations of the LBP Descriptor in Face Recognition

Fan

Arandjelović

2018

2018 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

The local binary descriptor (LPB) and its derivatives have a demonstrated track record of good performance in face recognition. Nevertheless the original descriptor, the framework within which it is employed, and the aforementioned improvements of these in the existing literature, all suffer from a number of inherent limitations. In this work we highlight these and propose novel ways of addressing them in a principled fashion. Specifically, we introduce (i) gradient based weighting of local descriptor contributions to region based histograms as a means of avoiding data smoothing by non-discriminative image loci, and (ii) Gaussian fuzzy region membership as a means of achieving robustness to registration errors. Importantly, the nature of these contributions allows the proposed techniques to be combined with the existing extensions to the LBP descriptor thus making them universally recommendable. Effectiveness is demonstrated on the notoriously challenging Extended Yale B face corpus.

show abstract

“…Xu et al attempt to produce and exploit reasonable virtual-training samples that can correctly model the variation of the face and provide a higher recognition accuracy [16][17][18]. On the other hand, finding local descriptors for facial features is one of the most popular approaches and favourable in those distributions of face images because they are less affected by changes in facial appearance [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Application of MEEMD in post‐processing of dimensionality reduction methods for face recognition

et al. 2018

View full text Add to dashboard Cite

Dimensionality reduction techniques are powerful tools for face recognition, because they obtain important information from a dataset. Several dimensionality reduction methods proposed in literature have been improved thanks to preprocessing approaches. However, they also require post-processing to rectify and increase the quality of projected data. This study presents a simple and new discriminative post-processing framework to make the dimensionality reduction methods robust to outliers. In detail, the proposed approach separates features according to their scale using multidimensional ensemble empirical mode decomposition (MEEMD) and then the spatial and frequency domain processing methods are employed to preserve crucial features. The performance of the proposed method is evaluated on ORL, Extended Yale B, AR, and LFW datasets by several dimensionality reduction techniques. The experimental results demonstrate that the proposed algorithm can perform very well in face recognition.

show abstract

Fusing landmark-based features at kernel level for face recognition

Cited by 13 publications

References 45 publications

Selective multi-descriptor fusion for face identification

Selective multi-descriptor fusion for face identification

Employing Domain Specific Discriminative Information to Address Inherent Limitations of the LBP Descriptor in Face Recognition

Application of MEEMD in post‐processing of dimensionality reduction methods for face recognition

Contact Info

Product

Resources

About