Distinctive Image Features from Scale-Invariant Keypoints
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76 citations
Additional excerpts
...First, we review handcrafted features, Scale Invariant Feature Transformation (SIFT) and Local Binary Patterns (LBP), which are both widely used in kinship verification [16] and facial recognition [20]....
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...While some relation are relatively easy to recognize, e.g., B-B, SIBS, S-S through SIFT, LBP, and VGG-Face features, results of other relations such as parent- child are still below 70.0%....
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...SIFT [15] features have been widely applied in object and face recognition....
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76 citations
Cites background from "Distinctive Image Features from Sca..."
...Dense SIFT descriptors are then extracted over the test face at multiple orientations....
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...Following the approach in [20], we quantize each SIFT descriptor using fast approximate k-means [16], which efficiently maps each descriptor to a visual word....
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...Each exemplar has four components: a face image, a set of dense quantized SIFT [15] features, a sparse set of semantic facial landmarks corresponding to mouth corners, nose tip, chin contour, etc., and a unique set of weights, one weight per {feature, landmark} pair....
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...Each exemplar has four components: a face image, a set of dense quantized SIFT [15] features, a sparse set of semantic facial landmarks corresponding to mouth corners, nose tip, chin contour, etc....
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76 citations
Cites methods from "Distinctive Image Features from Sca..."
...The technique proposed in [7] has treated ear as a planar surface and has created a homography transform using SIFT [19] feature points to register ears accurately....
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76 citations
Cites background from "Distinctive Image Features from Sca..."
...The descriptors are invariant to scale, rotation, lighting, noise and minor changes in viewpoint [24]....
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References
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