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.

PDF Abstract

Datasets


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

Methods


No methods listed for this paper. Add relevant methods here