Subtasks of Unconstrained Face Recognition

Unconstrained face recognition remains a challenging computer vision problem despite recent exceptionally high results (∼ 95% accuracy) on the current gold standard evaluation dataset: Labeled Faces in the Wild (LFW) (Huang et al., 2008; Chen et al., 2013). We offer a decomposition of the unconstrained problem into subtasks based on the idea that invariance to identity-preserving transformations is the crux of recognition. Each of the subtasks in the Subtasks of Unconstrained Face Recognition (SUFR) challenge consists of a same-different face-matching problem on a set of 400 individual synthetic faces rendered so as to isolate a specific transformation or set of transformations. We characterized the performance of 9 different models (8 previously published) on each of the subtasks. One notable finding was that the HMAX-C2 feature was not nearly as clutter-resistant as had been suggested by previous publications (Leibo et al., 2010; Pinto et al., 2011). Next we considered LFW and argued that it is too easy of a task to continue to be regarded as a measure of progress on unconstrained face recognition. In particular, strong performance on LFW requires almost no invariance, yet it cannot be considered a fair approximation of the outcome of a detection→alignment pipeline since it does not contain the kinds of variability that realistic alignment systems produce when working on non-frontal faces. We offer a new, more difficult, natural image dataset: SUFR-in-the-Wild (SUFR-W), which we created using a protocol that was similar to LFW, but with a few differences designed to produce more need for transformation invariance. We present baseline results for eight different face recognition systems on the new dataset and argue that it is time to retire LFW and move on to more difficult evaluations for unconstrained face recognition.

[1]  Joel Z. Leibo,et al.  Learning Generic Invariances in Object Recognition: Translation and Scale , 2010 .

[2]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[3]  George W. Quinn,et al.  Report on the Evaluation of 2D Still-Image Face Recognition Algorithms , 2011 .

[4]  H. Bülthoff,et al.  Face recognition under varying poses: The role of texture and shape , 1996, Vision Research.

[5]  Matti Pietikäinen,et al.  Multiscale Local Phase Quantization for Robust Component-Based Face Recognition Using Kernel Fusion of Multiple Descriptors , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Tomaso Poggio,et al.  CNS: a GPU-based framework for simulating cortically-organized networks , 2010 .

[7]  Ville Ojansivu,et al.  Blur Insensitive Texture Classification Using Local Phase Quantization , 2008, ICISP.

[8]  Nicolas Pinto,et al.  How far can you get with a modern face recognition test set using only simple features? , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Nicolas Pinto,et al.  Why is Real-World Visual Object Recognition Hard? , 2008, PLoS Comput. Biol..

[10]  James J. DiCarlo,et al.  How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.

[11]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[12]  Koen E. A. van de Sande,et al.  Empowering Visual Categorization With the GPU , 2011, IEEE Transactions on Multimedia.

[13]  Deva Ramanan,et al.  Face detection, pose estimation, and landmark localization in the wild , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Tal Hassner,et al.  Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Takeo Kanade,et al.  Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[16]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[17]  Jian Sun,et al.  Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  OjalaTimo,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002 .

[19]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[20]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[21]  Tomaso Poggio,et al.  Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.

[22]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[23]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Nicolas Pinto,et al.  Establishing Good Benchmarks and Baselines for Face Recognition , 2008 .

[25]  Thomas Serre,et al.  A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.

[26]  Nicolas Pinto,et al.  Comparing state-of-the-art visual features on invariant object recognition tasks , 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).

[27]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Takeo Kanade,et al.  Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[29]  郭頳 三维头像建模利器——FaceGen Modeller , 2003 .

[30]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[31]  Wendy L. Braje,et al.  Illumination effects in face recognition , 1998, Psychobiology.

[32]  Cordelia Schmid,et al.  Is that you? Metric learning approaches for face identification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[33]  Frédéric Jurie,et al.  Face Recognition using Local Quantized Patterns , 2012, BMVC.

[34]  Lorenzo Rosasco,et al.  The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work). , 2012 .