Recognition of blurred faces using Local Phase Quantization

Ahonen, Timo; Rahtu, Esa; Ojansivu, Ville; Heikkilä, Janne

doi:10.1109/icpr.2008.4761847

Cited by 238 publications

(175 citation statements)

References 5 publications

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“…The UTS system fuses the results of Gabor phases of 40 default Gabor wavelets [13] and local phase quantization (LPQ) features [17]. After performing a photometric normalization [9] on the aligned images, they are split into 7 × 7 local regions, in which histograms of LPQ patterns are extracted; and 8 × 8 regions for extracting histograms of Gabor phases.…”

Section: Fusion Systemsmentioning

confidence: 99%

The 2013 face recognition evaluation in mobile environment

Günther

Costa‐Pazo

Ding

et al. 2013

2013 International Conference on Biometrics (ICB)

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Automatic face recognition in unconstrained environments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification metrics. Most submitted algorithms rely on one or more of three types of features: local binary patterns, Gabor wavelet responses including Gabor phases, and color information. The best results are obtained from UNILJ-ALP, which fused several image representations and feature types, and UC-HU, which learns optimal features with a convolutional neural network. Additionally, we assess the usability of the algorithms in mobile devices with limited resources.

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Section: Fusion Systemsmentioning

confidence: 99%

The 2013 face recognition evaluation in mobile environment

Günther

Costa‐Pazo

Ding

et al. 2013

2013 International Conference on Biometrics (ICB)

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show abstract

“…It is difficult to draw broad conclusions as many images of the same people looked very similar [10]. In [10], Lades et al presented a dynamic link architecture for distortion invariant object recognition which employs elastic graph matching to find the closest stored graph. Sparse graphs whose vertices are labeled with a multi-resolution description in terms of a local power spectrum, and whose edges are labeled with geometrical distances.…”

Section: Literature Workmentioning

confidence: 99%

“…High-level recognition is typically modeled with many stages of processing as in Marr paradigm of processing from images to surfaces to three-dimensional (3D) models to matched models [10]. However, Turk and Pentland [18] argue that there is also a recognition process based on two-dimensional (2D) image processing.…”

Section: Literature Workmentioning

confidence: 99%

Deep Neural Network for Human Face Recognition

Gupta¹,

Saxena²,

Sharma³

et al. 2018

IJEM

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Face recognition (FR), the process of identifying people through facial images, has numerous practical applications in the area of biometrics, information security, access control, law enforcement, smart cards and surveillance system. Convolutional Neural Networks (CovNets), a type of deep networks has been proved to be successful for FR. For real-time systems, some preprocessing steps like sampling needs to be done before using to CovNets. But then also complete images (all the pixel values) are passed as input to CovNets and all the steps (feature selection, feature extraction, training) are performed by the network. This is the reason that implementing CovNets are sometimes complex and time consuming. CovNets are at the nascent stage and the accuracies obtained are very high, so they have a long way to go. The paper proposes a new way of using a deep neural network (another type of deep network) for face recognition. In this approach, instead of providing raw pixel values as input, only the extracted facial features are provided. This lowers the complexity of while providing the accuracy of 97.05% on Yale faces dataset.

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“…We used the local binary pattern histogram (LBPH) [1] and local phase quantisation pattern histogram (LPQH) [2] as descriptors, and the chi-squared distance is applied to measure the distance between gallery and probe descriptors.…”

Section: Face Descriptormentioning

confidence: 99%

Robust face recognition by an albedo based 3D morphable model

Chan

Yan

et al. 2014

IEEE International Joint Conference on Biometrics

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Large pose and illumination variations are very challenging for face recognition. The 3D Morphable Model (3DMM) approach is one of the effective methods for pose and illumination invariant face recognition. However, it is very difficult for the 3DMM to recover the illumination of the 2D input image because the ratio of the albedo and illumination contributions in a pixel intensity is ambiguous. Unlike the traditional idea of separating the albedo and illumination contributions using a 3DMM, we propose a novel Albedo Based 3D Morphable Model (AB3DMM), which removes the illumination component from the images using illumination normalisation in a preprocessing step. A comparative study of different illumination normalisation methods for this step is conducted on PIE and Multi-PIE databases. The results show that overall performance of our method outperforms state-of-the-art methods.

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Recognition of blurred faces using Local Phase Quantization

Cited by 238 publications

References 5 publications

The 2013 face recognition evaluation in mobile environment

The 2013 face recognition evaluation in mobile environment

Deep Neural Network for Human Face Recognition

Robust face recognition by an albedo based 3D morphable model

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