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Face detection using HTML5, javascript, webrtc, websockets, Jetty and OpenCV

Face detection using HTML5, javascript, webrtc, websockets, Jetty and OpenCV
Through HTML5 and the corresponding standards, modern browsers get more standarized features with every release. Most people have heard of websockets that allows you to easily setup a two way communication channel with a server, but one of the specifications that hasn't been getting much coverage is the webrtc specificiation. With the webrtc specification it will become easier to create pure HTML/Javascript real-time video/audio related applications where you can access a user's microphone or webcam and share this data with other peers on the internet. For instance you can create video conferencing software that doesn't require a plugin, create a baby monitor using your mobile phone or more easily facilitate webcasts. All using cross-browser features without the use of a single plugin. Update: in the newest versions of the webrtc spec we can also access the microphone! For this we need to take the following steps: Which tools and technologies do we use What do we use at the backend:

http://www.smartjava.org/content/face-detection-using-html5-javascript-webrtc-websockets-jetty-and-javacvopencv

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121VIEW Digital Signage Media Who We Are 121View is a digital media software development company. We provide two way digital signage media networks that interact with customers in the marketplace. The 80/20 Guide to Writing AngularJS Directives AngularJS is blowing up right now, and with good reason. There’s nothing more satisfying than using AngularJS to turn 1,000 messy lines of Backbone.js and jQuery spaghetti code into a trivial 10 lines. To put it in a broader context, you can think of AngularJS’ place in the world this way: AngularJS is to jQuery as C++11 is to x86 Assembly. How Face Detection Works OpenCV's face detector uses a method that Paul Viola and Michael Jones published in 2001. Usually called simply the Viola-Jones method, or even just Viola-Jones, this approach to detecting objects in images combines four key concepts: Simple rectangular features, called Haar features An Integral Image for rapid feature detection The AdaBoost machine-learning method A cascaded classifier to combine many features efficiently

How Police Listen to You Part 2: E911 Phone Tracking and How to Troll It How Police Listen to You Part 2: E911 Phone Tracking and How to Troll It Thibault Serlet What Are E911 Pings? Modern Enhanced 911 emergency systems (E911) systems automatically track the locations of people who call 911. Face Recognition with OpenCV — OpenCV v2.4.2 documentation Introduction OpenCV (Open Source Computer Vision) is a popular computer vision library started by Intel in 1999. The cross-platform library sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. In 2008 Willow Garage took over support and OpenCV 2.3.1 now comes with a programming interface to C, C++, Python and Android.

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Components Available glyphs Includes over 250 glyphs in font format from the Glyphicon Halflings set. Glyphicons Halflings are normally not available for free, but their creator has made them available for Bootstrap free of cost. Gender Classification with OpenCV — OpenCV v2.4.9 documentation Introduction A lot of people interested in face recognition, also want to know how to perform image classification tasks like: Gender Classification (Gender Detection)Emotion Classification (Emotion Detection)Glasses Classification (Glasses Detection)... This is has become very, very easy with the new FaceRecognizer class. In this tutorial I’ll show you how to perform gender classification with OpenCV on a set of face images.

blog:gender_classification [ My last post was very long and I promise to keep this one short. In this post I want to do gender classification on a set of face images and find out which are the specific features faces differ in. Dataset blog:fisherfaces [ Some time ago I have written a post on Linear Discriminant Analysis, a statistical method often used for dimensionality reduction and classification. It was invented by the great statistician Sir R. A. Fisher, who successfully used it for classifying flowers in his 1936 paper "The use of multiple measurements in taxonomic problems" (The famous Iris Data Set is still available at the UCI Machine Learning Repository.). But why do we need another dimensionality reduction method, if the Principal Component Analysis (PCA) did such a good job?

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