High-throughput-derived biologically-inspired features for unconstrained face recognition

  • Authors:

  • Nicolas Pinto;David D. Cox

  • Affiliations:

  • The Rowland Institute at Harvard, Harvard University, Cambridge, MA 02142, United States and McGovern Institute for Brain Research at MIT, Cambridge, MA 02139, United States;The Rowland Institute at Harvard, Harvard University, Cambridge, MA 02142, United States

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2012

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Abstract

Many modern computer vision algorithms are built atop of a set of low-level feature operators (such as SIFT [23,24]; HOG [8,3]; or LBP [1,2]) that transform raw pixel values into a representation better suited to subsequent processing and classification. While the choice of feature representation is often not central to the logic of a given algorithm, the quality of the feature representation can have critically important implications for performance. Here, we demonstrate a large-scale feature search approach to generating new, more powerful feature representations in which a multitude of complex, nonlinear, multilayer neuromorphic feature representations are randomly generated and screened to find those best suited for the task at hand. In particular, we show that a brute-force search can generate representations that, in combination with standard machine learning blending techniques, achieve state-of-the-art performance on the Labeled Faces in the Wild (LFW) [19] unconstrained face recognition challenge set. These representations outperform previous state-of-the-art approaches, in spite of requiring less training data and using a conceptually simpler machine learning backend. We argue that such large-scale-search-derived feature sets can play a synergistic role with other computer vision approaches by providing a richer base of features with which to work.