Face recognition with Convolutional Neural Network Martin Vels - PowerPoint PPT Presentation

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Face recognition with Convolutional Neural Network Martin Vels Face recognition with CNN Labeled Faces in the Wild (LFW) dataset with 13,233 images, 5749 persons (classes) Only using classes with 5 or more samples: 423 classes

Face recognition with Convolutional Neural Network Martin Vels
Face recognition with CNN ● Labeled Faces in the Wild (LFW) dataset with 13,233 images, 5749 persons (classes) ● Only using classes with 5 or more samples: 423 classes ● Using Convolutional Neural Network (CNN) to recognize person on the image
Motivation ● Face recognition and in general pattern recognition are interesting topic ● My research is related to analyzing video data to find certain patterns ● Video is a sequence of images ● Get to know the topic of CNN and use the knowledge in my research
Goal ● CNN can achieve really good results on image data ● Sample CIFAR-10 dataset with 60k images and 10 classes achieves <2% error rates ● With LFW dataset, achieving 30% error rate would be reasonable
LFW Dataset ● 423 classes, 5985 images, ● median number of images per class: 8, 50% of classes with 5 images, ● most images per class: 530 ● image size 250x250px ● cropping 128x128 from center, resizing to 64x64px ● some experiments with grayscale images
LFW Dataset - resizing
Convolutional Neural Network ● Similar to regular neural network ● Basic building block is neuron ● Neurons are organized into layers ● Various types of layers ● Idea is to gradually reduce high dimensional input and classify the image
Neuron - the main building block http://cs231n.github.io/neural-networks-1/
ConvNet architecture Various types of layers to reduce dimensions http://cs231n.github.io/convolutional-networks/
MatConvNet ● MATLAB toolbox ● Implements Convolutional Neural Networks for computer vision applications ● CNN building blocks available as functions ● Available freely: http://www.vlfeat. org/matconvnet/
Results 10 classes with at least 50 images per class 32x32 RGB 64x64 grayscale
Results 423 classes with mostly less than 8 images per class 32x32 RGB 64x64 grayscale
Conclusion ● CNN is an interesting and promising tool ● Works well with large dataset ● Disappointing results with my dataset Future ideas: ● Use horizontal flipping and cropping from corners to generate more data ● Experiment with different configurations and parameters

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