Proceedings ArticleDOI
A combined SIFT/SURF descriptor for automatic face recognition
Ladislav Lenc,Pavel Král +1 more
- Vol. 9067, pp 460-465
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TLDR
A novel approach which combines the SIFT and SURF features for the face representation is proposed and it is shown that this approach outperforms significantly all other methods on these corpora.Abstract:
This paper deals with Automatic Face Recognition (AFR). A novel approach which combines the SIFT and SURF features for the face representation is proposed. The obtained combined SIFT/SURF descriptor is then used for face comparison by the adapted Kepenekci matching method. The proposed method is evaluated on the FERET and CTK corpora. The obtained recognition rates are 98.4% and 64.6% respectively. These recognition scores show that our approach outperforms significantly all other methods on these corpora. The differences between recognition error rates of the proposed approach and the second best one are 41% and 7% in relative value respectively.read more
Citations
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Journal ArticleDOI
A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF.
Nouman Ali,Khalid Bashir Bajwa,Robert Sablatnig,Savvas A. Chatzichristofis,Zeshan Iqbal,Muhammad Rashid,Hafiz Adnan Habib +6 more
TL;DR: This paper presents a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF), which adds the robustness of both features to image retrieval.
Proceedings Article
Face Recognition under Real-world Conditions
Ladislav Lenc,Pavel Král +1 more
TL;DR: A confidence measure technique is proposed as a solution to identify and to filter-out the incorrectly recognized faces and it is demonstrated, that the recognition rate decreases significantly with larger database.
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Illumination, Pose and Occlusion Invariant Face Recognition from Range Images Using ERFI Model
TL;DR: The pivotal contribution of the authors is to recognize the 3D face images from range images in the unconstrained environment i.e. under varying illumination, pose as well as occlusion that are considered to be the most challenging task in the domain of face recognition.
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Robust approach of video steganography using combined keypoints detection algorithm against geometrical and signal processing attacks
TL;DR: From experimental results, it is observed that the PM outperforms contemporary methods by attaining significant outcomes and is evaluated in terms of the perceptual invisibility, robustness, and concealing capacity.
Book ChapterDOI
Classification of Neuroblastoma Histopathological Images Using Machine Learning
TL;DR: Using dataset gathered from The Tumour Bank at Kids Research at The Children’s Hospital at Westmead, which has been used in previous research, a range of feature extraction and data undersampling and over-sampling techniques are explored to improve classification accuracy.
References
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TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Proceedings ArticleDOI
Rapid object detection using a boosted cascade of simple features
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Proceedings ArticleDOI
Object recognition from local scale-invariant features
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Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Journal ArticleDOI
Speeded-Up Robust Features (SURF)
TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
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