Face Recognition in Low Resolution Images Trey Amador Scott Matsumura Matt Yiyang Yan
Introduction
Purpose: low resolution facial recognition Extract image/video from source Identify the person in real time given a traineddatabase taken from https://github.com/alexjc/neuralenhance
Face Recognition Libraries histogram of oriented gradients (HOG) dlib Support Vector Machines (SVM)
Process Neural Enhance library increase the resolution of low pixel density Theano (neural network) Lasagne (train) upsampled image dlib Histogram of oriented gradients (HOG) SVM feature descriptor for detecting faces
Database IMDb Internet Movie Database is an online database of information related to films, television programs and video games low and high resolution versions of the same image highresolution 'base' image to train the Support Vector Machine (SVM)
Support Vector Machine for Face Recognition Arnold Schwarzenegger
SVM Identify the Rock Image of Images similar to
SVM The Rock not The Rock Images similar to Image of
SVM Separate data Images similar to Image of
SVM Which line? Images similar to Image of
SVM Thickest line Images similar to Image of
SVM Separate data? Images similar to Image of
SVM Nonlinear separation Images similar to Image of
Generative Adversarial Network for Upsampling Images
GAN Back with The Rock Image of Images similar to
GAN Generate this image? Image of Images similar to
Generative Network produce an image Discriminative Network real or fake vs
How to train your Generative Adversarial Network
GAN Train discriminative network real Discriminative Network fake
GAN Train both networks random noise Generative Network Discriminative Network Fake negative gradient positive gradient backpropagation
GAN Eventually? random noise Generative Network Discriminative Network Real backpropagation
GAN Upsampled Generative Network Discriminative Network Real
Code can be found at: https://github.com/presidentcamacho/superresface
super resolution video samples
face recognition in enhancedresolution video
super resolution image enhancement boring Bruce Springsteen 100 x 100 enhanced Bruce Springsteen 200 x 200 actual Bruce Springsteen high res
super resolution face recognition unrecognized Bruce Springsteen 100 x 100 that s Bruce Springsteen! 200 x 200
experimental paradigm true face high res low res enhanced res false face high res low res enhanced res
future directions find robust metric with which to filter data test efficacy of various algorithms generate larger dataset
References [1] W. Zhao, et al. Face Recognition: A Literature Survey. ACM Computing Surveys, vol. 35, pp. 399458, Dec. 2003. [2] S.C. Park, M.K. Park, and M.G. Kang. SuperResolution Image Reconstruction: A Technical Overview. IEEE Signal Processing Magazine. May 2003. [3] D. Glasner, S. Bagon, and M. Irani. SuperResolution from a Single Image, in IEEE 12th ICCV, 2009, pp 349356. [4] W.W. Zou and P.C. Yuen. Very Low Resolution Face Recognition Problem. IEEE Transactions on Image Processing, vol. 21, pp. 327340, July 2012. [5] A. Geitgey, "Face Recognition," GitHub repository, [Online]. Available: https://github.com/ageitgey/face_recognition. [Accessed 29 10 2017]. [6] N. Dalal and B. Triggs. Histogram of Oriented Gradients for Human Detection in CVPR, 2005, pp. 18. [7] P. Felzenszwalb, et al. Object Detection with Discriminantly Trained Part Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, pp. 16271645, Sept. 2010. [8] C. Cortes and V. Vladimir, "SupportVector Networks," Machine Learning, vol. 20, no. 3, pp. 273297, 1995. [9] A. J. Champandard, "Neural Enhance," GitHub repository, [Online]. Available: https://github.com/alexjc/neuralenhance. [Accessed 29 10 2017]. [10] D. G. Lowe, "Object Recognition from Local ScaleInvariant Features," Computer Vision, vol. 2, pp. 11501157, 1999. [11] K. Simonyan, M. O. Parkhi, A. Vedaldi and A. Zisserman, "Fisher Vector Faces in the Wild," British Machine Vision Conference, vol. 2, no. 3, p. 4, Sept. 2013. [12] P. Fischer, A. Dosovitskiy and T. Brox, "Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT," arxiv, p. 10, 22 May 2014. [13] M. O. Parkhi, A. Vedaldi and A. Zisserman, "Deep Face Recognition," British Machine Vision Conference, vol. 1, no. 3, p. 6, 2015. [14] U. Karn, "An Intuitive Explanation of Convolutional Neural Networks," The Data Science Blog, [Online]. Available: https://ujjwalkarn.me/2016/08/11/intuitiveexplanationconvnets/. [Accessed 29 10 2017]. [15] C. Ledig, et al. "PhotoRealistic Single Image SuperResolution Using a Generative Adversarial Network," arxiv, p. 19, 25 May 2016. [16] A.V. Nefian. Georgia Tech Face Database. Nov. 15, 1999. [Online]. Available: www.anefian.com/research/face_reco.htm. [Accessed: Nov. 5, 2017]. [17] Y.D. Wong. ChokePoint Dataset. [Online]. Available: arma.sourceforge.net/chokepoint/. [Accessed: Nov. 5, 2017].