Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification

CVPR 2013  ·  Dong Chen, Xudong Cao, Fang Wen, Jian Sun ·

Making a high-dimensional (e.g., 100K-dim) feature for face recognition seems not a good idea because it will bring difficulties on consequent training, computation, and storage. This prevents further exploration of the use of a highdimensional feature. In this paper, we study the performance of a highdimensional feature. We first empirically show that high dimensionality is critical to high performance. A 100K-dim feature, based on a single-type Local Binary Pattern (LBP) descriptor, can achieve significant improvements over both its low-dimensional version and the state-of-the-art. We also make the high-dimensional feature practical. With our proposed sparse projection method, named rotated sparse regression, both computation and model storage can be reduced by over 100 times without sacrificing accuracy quality.

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Results from Other Papers

Task Dataset Model Metric Name Metric Value Rank Source Paper Compare
Age-Invariant Face Recognition CACDVS High-Dimensional LBP Accuracy 81.6 # 9


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