Presentation Attack Detection for Face Recognition Using Light Field Camera

Ramachandra, Raghavendra; Raja, Kiran B.; Busch, Christoph

doi:10.1109/tip.2015.2395951

Cited by 172 publications

(92 citation statements)

References 36 publications

(44 reference statements)

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“…Zhao et al [11] proposed to detect flat surface using the disparity map of the scene; however this method depends on the accuracy of disparity estimation and is sensitive to the alignment errors of disparity estimation. Raghavendra et al [15] proposed a similar strategy. Instead of the disparity map used in [11], they proposed to obtain a rough depth map by using the focus measure.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Different from the traditional "depth map to plane fitting" strategy [11,15,16], we detect the Lisad-1 feature point [17] in 4D light field and then fit the function of the flat surface by using several feature points in light field directly; finally the robustness of the function which we build is tested by another several feature points. If the scene is a flat surface, the function which is built from several feature points should also be suitable for other points and vice versa.…”

Section: Background and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Epipolar Plane Image Rectification and Flat Surface Detection in Light Field

Zhu

Wang

2017

Journal of Electrical and Computer Engineering

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Flat surface detection is one of the most common geometry inferences in computer vision. In this paper we propose detecting printed photos from original scenes, which fully exploit angular information of light field and characteristics of the flat surface. Unlike previous methods, our method does not need a prior depth estimation. The algorithm rectifies the mess epipolar lines in the epipolar plane image (EPI). Then feature points are extracted from light field data and used to compute an energy ratio in the depth distribution of the scene. Based on the energy ratio, a feature vector is constructed and we obtain robust detection of flat surface. Apart from flat surface detection, the kernel rectification algorithm in our method can be expanded to inclined plane refocusing and continuous depth estimation for flat surface. Experiments on the public datasets and our collections have demonstrated the effectiveness of the proposed method.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Epipolar Plane Image Rectification and Flat Surface Detection in Light Field

Zhu

Wang

2017

Journal of Electrical and Computer Engineering

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“…al. [12] used lytro light field camera (LFC) that captures the intensities and the direction of all possible rays incident on each photo sensor pixel. Hence, in a single capture, LFC provides multiple depth images.…”

Section: Related Workmentioning

confidence: 99%

Face Liveness Detection using Local Diffused Patterns

Pallavi¹,

Sharma²

2016

IJCA

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In general, face spoofing attacks deals with printing artifacts, electronic screens and ultra-realistic face masks or models. This paper proposes a liveness detection method based on diffusion speed. Diffusion speed of a single image is calculated as the difference of the original images and diffused images at each pixel. Face spoofing method based on diffusion speed does not require any user involvement and works with a single image. The key aspect of the proposed method is based on the difference in the illumination characteristics of live and fake faces. To solve the nonlinear, scalar valued diffusion equation, AOS (Additive Operator Splitting) approach, together with TDMA (Tri-Diagonal Matrix Algorithm) is applied. The local pattern of the diffusion speed is calculated at each pixel position (Local Diffused Patterns) and fed to linear Support Vector Machine for classification. Proposed approach performs well against the diverse malicious attacks, face display media (screen / paper) & varying illuminations and gives 90.83% accuracy.

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“…For this purpose, facial recognition is one of the biometric methods used to identify individuals on the basis of biological information, many studies being done, such as [3], [25], [37], [2] and [28]. Other methods belonging to this category are, for example, capture of the iris image [8] or fingerprints [13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Face Recognition Approach Based on Genetic Algorithm Optimization

Moussa¹,

Hmila²,

Douik³

2018

STUD INFORM CONTROL

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Abstract:In the field of image processing and recognition, discrete cosine transform (DCT) and principal component analysis (PCA) are two widely used techniques. In this paper we present a face recognition approach based on them. Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of a face recognition system. Genetic Algorithms (GA), one of the most recent techniques in the field of feature selection, are a type of evolutionary algorithms that can be used also to solve this issue. The application of a GA in the resolution of a problem requires the coding of the potential solutions to this problem in finite bit chains in order to constitute the chromosomes coming from a population formed by candidate points. The aim is to find a selective function allowing good discrimination between chromosomes and to define the genetic operators that will be used. In this sense, this approach seeks to develop a system of face recognition using Genetic Algorithm and a DCT-PCA combination for feature selection and dimensionality reduction, to be applied to an archive of images of human faces. The proposed approach is applied on various Face Databases. Experimental results demonstrate the effectiveness of this approach compared to state of the art in face recognition.

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Presentation Attack Detection for Face Recognition Using Light Field Camera

Cited by 172 publications

References 36 publications

Epipolar Plane Image Rectification and Flat Surface Detection in Light Field

Epipolar Plane Image Rectification and Flat Surface Detection in Light Field

Face Liveness Detection using Local Diffused Patterns

A Novel Face Recognition Approach Based on Genetic Algorithm Optimization

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