Face recognition with Deep Neural Network

Download Face recognition with Deep Neural Network

Post on 16-Apr-2017

441 views

Category:

Technology

4 download

TRANSCRIPT

(Face Recognition with Deep Neural Network)..*.**, , -, , *: , ., , -, , **: , , -, , 2016-11-30 , 99.5%, DeepFace 99.7%, EigenFace 64.8% : ImageNet : GPU, CUDA, cuDNN, Caffe, Torch , , CS131: Computer Vision: Foundations and Applications Brandon Amos: , , , , CT, MRI ( ) , , , , , ... Nvidia.com, Omate.com , Computer Vision, Image Classification, Machine & Deep Learning, CNN, RNN, Softmax, SVM, .. , , (ImageNet 14 ) ... : GPU, , .. : Numpy, Scikit-learn, Linux, OpenCV, Dlib, CUDA, cuDNN, Caffe, Torch, TensorFlow, .. , , , , , NVIDIA GPU Educators Program, AI & Autonomous Robotic , . AI Robot of smart home Pre-trained VGG models by Oxford University, * , ( Face Detection and Recognition: Theory and Practice ) : HOG Histogram of Oriented Gradients 1. 2. HOG 3. HOG Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning Convolutional, Non-linear, Pooling (Down sampling), Fully Connected layers, Output A Beginner's Guide To Understanding Convolutional Neural Networks CNN: (Convolving) A Beginner's Guide To Understanding Convolutional Neural Networks CNN: (neuron) A Beginner's Guide To Understanding Convolutional Neural Networks CNN: (feature) A Beginner's Guide To Understanding Convolutional Neural Networks Andrew NgCNN: Backpropagation Forward pass, Lost function, Backward pass, Weight update MSE (Mean Squared Error), Softmax, SVM, Gradient descent, Epoch A Beginner's Guide To Understanding Convolutional Neural Networks : 4 226 , 50 Dlib, OpenCV, Python : , Python, OpenCV, Dlib, Torch, OpenFace, CUDA, cuDNN, LinuxMintHOG face detection, pre-trained CNN model with Linear SVM classifier 92.54%, DeepFace 99.97%, 99.5% (True positive) , 2 50 0.83 0.87 0.91 0.98 0.86 0.97 0.97 0.99 0.97 0.92 0.927 0, 8 68 0.99 0.99 0.68 0.97 0.99 0.94 1.00 1.00 0.99 0.48 0.903 2 , 26 0.92 0.95 0.90 0.78 0.90 0.93 0.87 0.97 0.96 0.93 0.911 1 , 82 0.83 0.98 0.99 0.91 0.93 0.98 0.99 0.98 1.00 0.99 0.958 0 226 0.925 3 (True negative) 10 0.49 0.59 0.42 0.21 0.28 0.53 0.03 0.06 0.64 0.54 0.379 6 , , , , , (noise) , 92.5% ( 500-1000) GPU , - [1] Baidus Artificial-Intelligence Supercomputer Beats Google at Image Recognition, MIT Technology Review, 2015 [2] DeepFace: Closing the Gap to Human-Level Performance in Face Verification. Facebook AI Research Publication, 2014 [3] Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning, 2016 [4] Navneet Dalal, Bill Triggs. "Histograms of Oriented Gradients for Human Detection, 2005 [5] Vahid Kazemi, Josephine Sullivan. One Millisecond Face Alignment with an Ensemble of Regression Trees, 2014 [6] Florian Schroff, Dmitry Kalenichenko, James Philbin. FaceNet: A Unified Embedding for Face Recognition and Clustering, 2015 [7] Brandon Amos. OpenFace. https://cmusatyalab.github.io/openface/ [8] D. A. Forsyth and J. Ponce. "Computer Vision: A Modern Approach (2nd edition)". Prence Hall, 2011 [9] opencv.org, dlib.com, http://torch.ch [10] CUDA, cuDNN. http://nvidia.com [11] CS231n Convolutional Neural Networks for Visual Recognition, Stanford University [12] Stan Z. Li Anil K. Jain. Handbook of Face Recognition. Springer, 2004 [13] Asit Kumar Datta, Madhura Datta, Pradipta Kumar Banerjee. Face Detection and Recognition: Theory and Practice. Taylor & Francis, 2015 [14] Mohamed Daoudi, Anuj Srivastava, Remco Veltkamp. 3D Face Modeling, Analysis and Recognition. Wiley, 2013 !?Engineers turn dreams into realityHayao MiyazakiSlide 1Slide 2Slide 3Slide 4Slide 5Slide 6Slide 7Slide 11Slide 12Slide 13Slide 16Slide 17Slide 18Slide 19Slide 20Slide 21Slide 22Slide 23

Recommended

View more >