Copy-Move Forgery Detection (CMFD) is a well-studied image forensics problem. However, CMFD with Similar but Genuine Objects (SGO) has received relatively less attention. Recently, it has been found that current state...
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ISBN:
(纸本)9781509021758
Copy-Move Forgery Detection (CMFD) is a well-studied image forensics problem. However, CMFD with Similar but Genuine Objects (SGO) has received relatively less attention. Recently, it has been found that current state-of-the-art CFMD techniques are mostly inadequate in satisfactorily solving this important problem variant. In this paper, we have addressed this issue by using rotated local binary pattern (RLBP) based rotation-invariant texture features, followed by Generalized Two Nearest Neighbourhood (g2NN) based feature matching, hierarchical clustering and geometric transformation estimation. Experimental results show that our technique outperforms the state-of-the-art CFMD techniques for forged images having similar but genuine objects, and matches the accuracy of state-of-the-art techniques for other copy-move forgery types. Our method is also robust with respect to filtering and compression based post-processing.
Automatic facial expression recognition is a widely studied problem hi computer vision and human-robot interaction. There has been a range of studies for representing facial descriptors for facial expression recogniti...
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Automatic facial expression recognition is a widely studied problem hi computer vision and human-robot interaction. There has been a range of studies for representing facial descriptors for facial expression recognition. Some prominent descriptors were presented in the first facial expression recognition and analysis challenge (FERA2011). In that competition, the local Gabor binarypattern Histogram Sequence descriptor showed the most powerful description capability. In this paper, we introduce hybrid facial representations for facial expression recognition, which have more powerful description capability with lower dimensionality. Our descriptors consist of a block-based descriptor and a pixel-based descriptor. The block-based descriptor represents the micro-orientation and micro-geometric structure information. The pixel-based descriptor represents texture information. We validate our descriptors on two public databases, and the results show that our descriptors perform well with a relatively low dimensionality.
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