Investigating Nuisance Factors in Face Recognition with DCNN Representation

Deep learning based approaches proved to be dramatically effective to address many computer vision applications, including "face recognition in the wild". It has been extensively demonstrated that methods exploiting Deep Convolutional Neural Networks (DCNN) are powerful enough to overcome to a great extent many problems that negatively affected computer vision algorithms based on hand-crafted features. These problems include variations in illumination, pose, expression and occlusion, to mention some. The DCNNs excellent discriminative power comes from the fact that they learn low-and high-level representations directly from the raw image data. Considering this, it can be assumed that the performance of a DCNN are influenced by the characteristics of the raw image data that are fed to the network. In this work, we evaluate the effect of different bounding box dimensions, alignment, positioning and data source on face recognition using DCNNs, and present a thorough evaluation on two well known, public DCNN architectures.

[1]  Alberto Del Bimbo,et al.  Using 3D Models to Recognize 2D Faces in the Wild , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[2]  Gérard G. Medioni,et al.  Pose-Aware Face Recognition in the Wild , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Oscar Déniz-Suárez,et al.  Face recognition using Histograms of Oriented Gradients , 2011, Pattern Recognit. Lett..

[4]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.

[5]  Carlos D. Castillo,et al.  Triplet probabilistic embedding for face verification and clustering , 2016, 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[6]  Jude Shavlik,et al.  Chapter 11 Transfer Learning , 2009 .

[7]  Josephine Sullivan,et al.  One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Anil K. Jain,et al.  Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Tal Hassner,et al.  Face recognition in unconstrained videos with matched background similarity , 2011, CVPR 2011.

[10]  Cong Geng,et al.  SIFT features for face recognition , 2009, 2009 2nd IEEE International Conference on Computer Science and Information Technology.

[11]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Jun-Cheng Chen,et al.  An End-to-End System for Unconstrained Face Verification with Deep Convolutional Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[13]  Mostafa Mehdipour-Ghazi,et al.  A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[14]  Xiaogang Wang,et al.  Deep Learning Face Representation from Predicting 10,000 Classes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Rama Chellappa,et al.  Unconstrained face verification using deep CNN features , 2015, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

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

[17]  Dongqing Zhang,et al.  Neural Aggregation Network for Video Face Recognition , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Zhenan Sun,et al.  A Lightened CNN for Deep Face Representation , 2015, ArXiv.

[19]  Alberto Del Bimbo,et al.  Dictionary Learning Based 3D Morphable Model Construction for Face Recognition with Varying Expression and Pose , 2015, 2015 International Conference on 3D Vision.

[20]  Shengcai Liao,et al.  Learning Face Representation from Scratch , 2014, ArXiv.

[21]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Qiong Cao,et al.  Template Adaptation for Face Verification and Identification , 2016, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[23]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[24]  Andrew Zisserman,et al.  "Who are you?" - Learning person specific classifiers from video , 2009, CVPR.

[25]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[26]  Alberto Del Bimbo,et al.  Effective 3D based frontalization for unconstrained face recognition , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[27]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Honglak Lee,et al.  Learning to Align from Scratch , 2012, NIPS.

[29]  Carlos D. Castillo,et al.  An All-In-One Convolutional Neural Network for Face Analysis , 2016, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).