The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Beyond simple features: A large-scale feature search approach to unconstrained face recognition
Abstract
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] 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.
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Cites
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Histograms of Oriented Gradients for Human Detection
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Face Recognition with Local Binary Patterns
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What is the best multi-stage architecture for object recognition?
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Self-citationCortical neurons: Isolation of contrast gain control
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Cited by
Deep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578
PyCUDA and PyOpenCL: A scripting-based approach to GPU run-time code generation
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Self-citationDeep Representations for Iris, Face, and Fingerprint Spoofing Detection
Menotti, D., Chiachia, G., Pinto, A., Robson Schwartz, W., Pedrini, H., Xavier Falcao, A., & Rocha, A. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2398817 · Cited by 485
Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images
Vig, E., Dorr, M., & Cox, D. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.358 · Cited by 428
Self-citationHybrid Deep Learning for Face Verification
Sun, Y., Wang, X., & Tang, X. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.188 · Cited by 285
Metadata
- Authors
- Cox, D., & Pinto, N.
- Venue
- Face and Gesture 2011
- Publisher
- IEEE
- Published
- 2011-03
- Pages
- 8-15
- OpenAlex
- W2142947774
- Cited by
- 197
- References
- 9
- FWCI
- 18.65
- Type
- conference paper
- Language
- en
- Open access
- closed
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