A Proposal Defense On Deep Residual Network For Face Recognition Presented By SAGAR MISHRA MECE 15945

Face Recognition  Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult.  It is a very active research topic because of its application in human-robot interaction, human- machine interfaces, driving safety, Security.  Despite of the significant improvements, face recognition is still a challenging problem that wait for more and more accurate algorithms.

WHY DEEP RESIDUAL NETWORK ??

Problem Statement  The deeper the network can cover more complex problems  This is supposed to enhance the accuracy of the network.  However, training the deeper network is more difficult because of two problem  Vanishing Gradients : neuron can “die” in the training and become useless. This can cause information loss  Optimization Difficulty : weights, biases increases due to increasing depth, training the network becomes very difficult.

Consequences of more deeper network  When the number of layer increased than, there is increment in training error and test error  Kaming He el. a [2] present the increment of layers in plain Convolutional network from 20 t0 56 and shows the increment of errors.

Deep residual Learning  Plain net  H(x) is any desired mapping, hope the 2 weight layer fit H(x)  Residual nets  H(x)=F(x)+x, F(x) is residual mapping

Research Objective  To design a Face recognition system using Deep Residual Network  To compare the performance of this network with the previous counterparts VGG-Face and best result of paper "Deeply learned face representations are sparse, selective, and robust“  The Face recognition system will decrease training period and training error using deep residual network.  The comparison is based on following parameters  Accuracy  Training period  Training error

System Model

Face Detection  Detecting face in the set of images is the first step  In our model we are going to use Histograms of Oriented Gradients (HOG)  Steps for HOG transform  Make image black and white  For every single pixel look at the pixel that directly surround it.  Find the direction in which pixel gets darker  Every pixel is replaced with arrow called gradients

Preprocessing  Faces can be aligned in different ways  To account for this face landmark estimation algorithm invented in 2014 by Vahid Kazemi and Josephine Sullivan [5] will be used in our model.  The basic idea is to identify 68 landmark points that exist in every face  Finally Affine Transformation will be used to center the nose, eyes and mouth.

Feature Extraction  Residual network will be used to train with available dataset and extract features for individual image  residual network with depth of 19 layers will be trained. VGG-19 net will also be trained as a contrast for comparison purpose.

Deep Residual Block(ResBlock)

Convolution Neural Network  Consists of input, output and hidden layers  Hidden layers are Convolutional, Pooling and Fully connected  Convolutional Layer  Performs convolution and send output to next layer  Consists of filters  Filters are convoluted across length and width of input image to generate feature map  Its is simply dot product

Convolution Neural Network  Pooling Layer  Also called down sampling  Combines output of neuron clusters at one layer into a single neuron in next layer  Max pooling uses maximum value from each of cluster of neuron.  Fully connected layer  Traditional Multi Layer Perceptron acts as output layer  Connects every neuron in one layer to every neuron in another layer

Convolutional Neural Network

Rectified Linerar Unit (ReLU)  element wise operation (applied per pixel) and replaces all negative pixel values in the feature map by zero

Face Recognition  In the final step featured extracted from the image under test must be compared with the features stored in the face database to identify the face.

Training the network  The entire network will be trained with Stochastic Gradient Descent (SGD) with backpropagation.

Training Dataset  Dataset that will be used is LFW (Labeled Face in the Wild)  Consists of faces  Each face labeled with name of the person

Proposed Model

Performance Metric  The result of this model will be comparison with VGG-Face and best result of the paper [6]. Our model will be compared with previous models against accuracy, training error, training period.

Tools  The tools, programming language and software’s that will be used in this thesis work are listed below:  Python programming Language  Eclipse (pycharm)  Caffe/ Keras platform

Expected Output  Implementation of deep residual network for face recognition  The result of the residual network will be evaluated and compared with other best models against accuracy, training error, training period

Schedule TasksMonth/Year(2017/2018) SepOctNovDecJanFeb Literature Review Proposal Defense System Design And Coding Mid-Term Defense Final Submission of Thesis Documentation of Thesis Research and Experiments

References  [1] X. Z. S. R. He, Kaiming and J. Sun, “Deep residual learning for image recog-nition,”ICCV,  [2] A. D. B. Steve Lawrence, “Face recognition: A convolutional neural networkapproach,”IEEE, 1997  [3] J. D. S. K. Y. Jia, E. Shelhamer, “Caffe: Convolutional architecture for fastfeature embedding,”arXiv: , 2014  [4] Z. C. Xiu Li, “Deep redisual network for plank classification,” ICCV,  [5] A. Z. K. Simonyan, “Very deep convolutional networks for large- scale image recognition,”ICLR, , 6  [6] X. W. Y. Sun and X. Tang, “Deeply learned face representations are sparse, selective, and robust,”CoRR,abs/ , 2014  [7] S. I. Krizhevsky, A. and G. E. Hinton, “Imagenet classification with deep convolutional neural networks.” NIPS, 2012  [8] H. W. DMasaki Nakada and D. Terzopoulos, “Acfr:active face recognition using convolutional neural network,” 2017