Beyond simple features: A large-scale feature search approach to unconstrained face recognition

— Many modern computer vision algorithms are built atop of a set of low-level feature operators (such as SIFT [1], [2]; HOG [3], [4]; or LBP [5], [6]) 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) [7] unconstr...

• Type: Journal
• Year: 2011
• Where: FGR
• Authors: David D. Cox, Nicolas Pinto
• Keywords: Biometrics, Feature Representations, FGR 2011, Large-scale Feature Search, Low-level Feature Operators