Adaptation of SIFT Features for Robust Face Recognition

Krizaj, Janez; Štruc, Vitomir; Pavešić, Nikola

doi:10.1007/978-3-642-13772-3_40

Cited by 65 publications

(51 citation statements)

References 12 publications

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“…One of the most famous of these algorithms is the SIFT, which detects interest points, specifically keypoints, and assigns orientation properties to each keypoint based on the direction of the local gradient. In the field of facial recognition, the SIFT can detect specific facial features and performs well [11][12][13][14][15]. In [14], the SIFT was used to extract 3D geometry information from 3D face shapes to recognize 3D facial expressions.…”

Section: Research Backgroundmentioning

confidence: 99%

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Artwork Identification for 360-Degree Panoramic Images Using Polyhedron-Based Rectilinear Projection and Keypoint Shapes

Jin

Kim

2017

Applied Sciences

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Abstract:With the increased development of 360-degree production technologies, artwork has recently been photographed without authorization. To prevent this infringement, we propose an artwork identification methodology for 360-degree images. We transform the 360-degree image into a three-dimensional sphere and wrap it with a polyhedron. On the sphere, several points are located on the polyhedron to determine the width, height, and direction of the rectilinear projection. The 360-degree image is divided and transformed into several rectilinear projected images to reduce the adverse effects from the distorted panoramic image. We also propose a method for improving the identification precision of artwork located at a highly distorted position using the difference of keypoint shapes. After applying the proposed methods, identification precision is increased by 45% for artwork that is displayed on a 79-inch monitor in a seriously distorted position with features that were generated by scale-invariant feature transformations.

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Section: Research Backgroundmentioning

confidence: 99%

“…That point and the V 1 are also the points V f and V g . Therefore, the height hv for the viewing range can be computed with Equation (13). For both the 32-hedron and the dodecahedron, the determines the direction of the rectilinear projection.…”

Section: Appl Sci 2017 7 528 7 Of 25mentioning

confidence: 99%

Artwork Identification for 360-Degree Panoramic Images Using Polyhedron-Based Rectilinear Projection and Keypoint Shapes

Jin

Kim

2017

Applied Sciences

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“…There are various feature extraction methods in literature [16]. In this study, we used Scale Invariant Feature Transform method [17]. After extracted SIFT features, it is searched corresponding interest points from second image to interest points from first image according to similarity [18].…”

Section: Image Mosaicingmentioning

confidence: 99%

Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems

Yetiş¹,

Karaköse²

2016

International Journal of Applied Mathematics, Electronics and Computers

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Continuity of production is a highly important in the days that manufacturing is becoming bigger and serial. The mistakes done while producing process cause fail on products and it may bring about even big losses for the facility. Furthermore, hitches on robots at production line may also cause crucial damages that may give rise to high repair costs and discontinuance of production. In this study, it is aimed to obtain alive bird's eye view map of production lines, which are big and impossible to be monitored with only one camera, by using multi cameras and stitching algorithms. Finding the similar scenes of input images, estimation of homography, warping and blending operations, which are the steps used in feature based image-stitching algorithms, are applied respectively on images that are taken by cameras. The assignment of second nearest neighbor distance rate adaptively makes the results more qualified. After obtaining single stitched image movement detection is actualized by using the difference of sequential frames, and anomaly movements are determined. As a result, the robots at the long production lines can be monitored in one screen, and with processing the obtained image, faults on robots that may cause damage at non-cheap machines can be handled before time.

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“…There have been a lot of methods proposed for overcoming the difficulty of face recognition.These methods can be roughly divided into two groups:one is the global feature extraction method, such as the principal component analysis(PCA) [2]and the linear discriminant analysis(LDA) [3],etc, and the other is the local feature extraction method, such as scale invariant feature Transform [4], local binary pattern [5],etc.…”

Section: Introductionmentioning

confidence: 99%

Face Description with Monogenic Ternary Pattern: Application to Face Recognition

He¹,

Wang²

2015

Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication

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Adaptation of SIFT Features for Robust Face Recognition

Cited by 65 publications

References 12 publications

Artwork Identification for 360-Degree Panoramic Images Using Polyhedron-Based Rectilinear Projection and Keypoint Shapes

Artwork Identification for 360-Degree Panoramic Images Using Polyhedron-Based Rectilinear Projection and Keypoint Shapes

Adaptive Vision Based Condition Monitoring and Fault Detection Method for Multi Robots at Production Lines in Industrial Systems

Face Description with Monogenic Ternary Pattern: Application to Face Recognition

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