Proceedings ArticleDOI
Face recognition using SURF features
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TLDR
This paper proposes to exploit SURF features in face recognition in this paper by exploiting the advantages of SURF, a scale and in-plane rotation invariant detector and descriptor with comparable or even better performance with SIFT.Abstract:
The Scale Invariant Feature Transform (SIFT) proposed by David G. Lowe has been used in face recognition and proved
to perform well. Recently, a new detector and descriptor, named Speed-Up Robust Features (SURF) suggested by
Herbert Bay, attracts people's attentions. SURF is a scale and in-plane rotation invariant detector and descriptor with
comparable or even better performance with SIFT. Because each of SURF feature has only 64 dimensions in general and
an indexing scheme is built by using the sign of the Laplacian, SURF is much faster than the 128-dimensional SIFT at
the matching step. Thus based on the above advantages of SURF, we propose to exploit SURF features in face
recognition in this paper.read more
Citations
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Journal ArticleDOI
Face Recognition Systems: A Survey
TL;DR: This survey is to review some well-known techniques for each approach and to give the taxonomy of their categories and a solid discussion is given about future directions in terms of techniques to be used for face recognition.
Journal ArticleDOI
Reviewing ensemble classification methods in breast cancer.
Mohamed Hosni,Ibtissam Abnane,Ali Idri,Juan Manuel Carrillo de Gea,José Luis Fernández Alemán +4 more
TL;DR: This study found that of the six medical tasks that exist, the diagnosis medical task was that most frequently researched, and that the experiment-based empirical type and evaluation-based research type were the most dominant approaches adopted in the selected studies.
Journal ArticleDOI
2D-human face recognition using SIFT and SURF descriptors of face’s feature regions
TL;DR: The authors have presented the feature-based method for 2D face images, which uses speeded up robust features (SURF) and scale-invariant feature transform (SIFT) for feature extraction and has a maximum recognition accuracy of 99.7%.
Journal ArticleDOI
Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images.
Yoga Dwi Pranata,Kuan-Chung Wang,Jia-Ching Wang,Irwansyah Idram,Jiing Yih Lai,Jia-Wei Liu,I-Hui Hsieh +6 more
TL;DR: Results from real patient fracture data sets demonstrate the feasibility using deep CNN and SURF for computer-aided classification and detection of the location of calcaneus fractures in CT images.
Journal ArticleDOI
An evaluation of image-based structural health monitoring using integrated unmanned aerial vehicle platform
TL;DR: It has been shown that the proposed system can perform image stitching even if the UAV suffers angular displacement due to wind thrusts or calibration issues, and the displacement detected on the column of the structure's backyard verified the feasibility for real‐world SHM.
References
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
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
Object recognition from local scale-invariant features
TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
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.
Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.