Real-Time Tracking Of Human Eyes Using a Camera



Eyes are the most important features of the human face. So effective usage of eye movements as a communication technique in user-to-computer interfaces can find place in various application areas.

Eye tracking and the information provided by the eye features have the potential to become an interesting way of communicating with a computer in a human-computer interaction (HCI) system. So with this motivation, designing a real-time eye feature tracking software is the aim of this project.

The purpose of this demonstration is to implement a real-time eye-feature tracker with the following capabilities:

  • RealTime face tracking with scale and rotation invariance
  • Tracking the eye areas individually
  • Tracking eye features
  • Eye gaze direction finding
  • Remote controlling using eye movements


Sample Image - maximum width is 600 pixels

A chatterbox application with speech synthesis and speech recognition tacked on to it. Demonstrating how to build increasingly sophisticated recognition rules.


PatternRecognizer is a fast machine learning algorithm library . It contains support vector machine, neural networks, bayes, boost, k-nearest neighbor, decision tree, ..., etc.

This sample demonstrates learning chess board pattern with support vector machine, chess board pattern is a 640 random points formed synthetic dataset.


legend: squares are support vectors.


Automating Semantic Mapping of a Document With Natural Language Processing

Natural Language Processing (NLP) intends to enable computers to derive meaning from human or natural language input.  This demonstration extracts entities, keywords, topics, events, themes and concepts.  Other than themes and concepts, the results are essentially keywords or phrases.  The extracted "strings" often have an associated relevance or strength, count or frequency, and/or sentiment value.  We used the features of our NLP Engine to provide some filtering capabilities of RSS feeds, enabling the user to create filters based on the extracted strings and additional values.


BAOA (Big Data Online Analysis)

BAOA is the most popular for data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.



BAOA (Big Data On-line Analysis) is a framework for data stream mining. It includes tools for evaluation and a collection of machine learning algorithms. The goal of BAOA is a benchmark framework for running experiments in the data stream mining context by proving

  • storable settings for data streams (real and synthetic) for repeatable experiments
  • a set of existing algorithms and measures form the literature for comparison and
  • an easily extendable framework for new streams, algorithms and evaluation methods.



The workflow in BAOA follows the simple schema depicted below: first a data stream (feed, generator) is chosen and configured, second an algorithm (e.g. a classifier) is chosen and its parameters are set, third the evaluation method or measure is chosen and finally the results are obtained after running the task.



To run an experiment using BAOA, the user can choose between a graphical user interface (GUI) or a command line execution. BAOA currently supports stream classification, stream clustering, outlier detection, change detection and concept drift and recommender systems. We are working on extending BAOA to support other mining tasks on data streams.



Mouse Gesture Recognition


Learning and recognition of mouse gestures using hidden Markov model-based classifiers and Hidden Conditional Random Fields.


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