Gabor Ordinal Measures for Face Recognition

Chai, Zhenhua; Sun, Zhenan; Méndez-Vázquez, Heydi; He, Ran; Tan, Tieniu

doi:10.1109/tifs.2013.2290064

Cited by 114 publications

(67 citation statements)

References 41 publications

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“…Face images were resized to three different sizes as follows: 126 × 126, 94 × 94, and 70 × 70. For the 126 × 126 image, the eye coordinates were set to (50,66) and (75,66). When facial images are used in larger sizes, identification performance is adversely affected because low-resolution images reduce between-class differences.…”

Section: Experiments On Scfacementioning

confidence: 99%

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

2018

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Abstract:In this study, we propose a face recognition scheme using local Zernike moments (LZM), which can be used for both identification and verification. In this scheme, local patches around the landmarks are extracted from the complex components obtained by LZM transformation. Then, phase magnitude histograms are constructed within these patches to create descriptors for face images. An image pyramid is utilized to extract features at multiple scales, and the descriptors are constructed for each image in this pyramid. We used three different public datasets to examine the performance of the proposed method:Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), and Surveillance Cameras Face (SCface). The results revealed that the proposed method is robust against variations such as illumination, facial expression, and pose. Aside from this, it can be used for low-resolution face images acquired in uncontrolled environments or in the infrared spectrum. Experimental results show that our method outperforms state-of-the-art methods on FERET and SCface datasets.

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Section: Experiments On Scfacementioning

confidence: 99%

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

2018

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“…As in (Xie et al, 2010), FLD is applied to form local Gabor features. Besides, face identification can be built on other types of local representations, exemplified as GOM (Chai et al, 2014) and SLF .…”

Section: Related Workmentioning

confidence: 99%

“…Fusing other features that are independent of the local Gabor features can also lead to better performance: (Tan and Triggs, 2007;Su et al, 2009;Zhang et al, 2007) fuses the global (holistic) features with local ones at feature level; (Xie et al, 2010) proposes fusion of Gabor phase and amplitude on the score and feature levels; (Chai et al, 2014) fuses real, imaginary, amplitude and phase. Alternatively, attaching an illumination normalization step and weighting the local Gabor features is shown to be helpful as well (Cament et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

“…This is probably why progress in face identification has been relatively insignificant over the last five years. Though the proposed face identification approaches have become increasingly complex, recognition performance according to the benchmark evaluations remained relatively constant (Xie et al, 2010;Yang et al, 2013;Cament et al, 2014;Chai et al, 2014). To make a breakthrough in face identification, it seems we must revisit the foundation of face recognition, and have a fresh look at the fundamental building blocks of face recognition.…”

Section: Introductionmentioning

confidence: 99%

“…This is because the amplitude varies slowly with spatial shift, making it robust to texture variations caused by dynamic expressions and imprecise alignment. By constructing LBP-type features mostly from the amplitude and applying various learning techniques, many Gabor based approaches have shown remarkable advantages over pixel-featured based methods: the identification rate in benchmark evaluations has been found to be improved by more than 20% (reaching around 90%) thanks to the "blessing of dimensionality" (Givens et al, 2013) ( but at the high cost of computational efficiency (Mu et al, 2011;Chai et al, 2014)). Now the question is how to achieve face identification rates in the range from 90% to 95% or even higher.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Leveraging Gabor Phase for Face Identification in Controlled Scenarios

Yang

Liu

2016

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

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Abstract:Gabor features have been widely employed in solving face recognition problems in controlled scenarios. To construct discriminative face features from the complex Gabor space, the amplitude information is commonly preferred, while the other one -the phase -is not well utilized due to its spatial shift sensitivity. In this paper, we address the problem of face recognition in controlled scenarios. Our focus is on the selection of a suitable signal representation and the development of a better strategy for face feature construction. We demonstrate that through our Block Matching scheme Gabor phase information is powerful enough to improve the performance of face identification. Compared to state of the art Gabor filtering based approaches, the proposed algorithm features much lower algorithmic complexity. This is mainly due to our Block Matching enables the employment of high definition Gabor phase. Thus, a single-scale Gabor frequency band is sufficient for discrimination. Furthermore, learning process is not involved in the facial feature construction, which avoids the risk of building a database-dependent algorithm. Benchmark evaluations show that the proposed learning-free algorithm outperforms state-of-the-art Gabor approaches and is even comparable to Deep Learning solutions.

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Cancelable Palmprint Feature Generation Method Based on Minimum Signature

Qiu

Wang

2019

Communications in Computer and Information Science

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Gabor Ordinal Measures for Face Recognition

Cited by 114 publications

References 41 publications

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition

Leveraging Gabor Phase for Face Identification in Controlled Scenarios

Cancelable Palmprint Feature Generation Method Based on Minimum Signature

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