Conference Paper

Sparsely Encoded Local Descriptor for face recognition

Key Lab. of Intell. Inf. Process., Chinese Acad. of Sci. (CAS), Beijing, China
DOI: 10.1109/FG.2011.5771389 Conference: Automatic Face & Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on
Source: IEEE Xplore


In this paper, a novel Sparsely Encoded Local Descriptor (SELD) is proposed for face recognition. Compared with K-means or Random-projection tree based previous methods, sparsity constraint is introduced in our dictionary learning and sequent image encoding, which implies more stable and discriminative face representation. Sparse coding also leads to an image descriptor of summation of sparse coefficient vectors, which is quite different from existing code-words appearance frequency(/histogram)-based descriptors. Extensive experiments on both FERET and challenging LFW database show the effectiveness of the proposed SELD method. Especially on the LFW dataset, recognition accuracy comparable to the best known results is achieved.

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Conference Paper: Sparsely Encoded Local Descriptor for face recognition

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    • "Answer: We thank the reviewer's comment. We have made a more comprehensive discussion of previous work [2] and [4] to address the difference and the novelty of our method in Section II. To be more specific, we have replaced Section II with the following text: A recent work \cite{guoWCD12} measures similarity and dissimilarity scores of sparse codes from an image pair based on a reference set of images to build an augmented dictionary. "
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