Human Emotion Detection from Image Download source - 2.46 MB Introduction This code can detect human emotion from image. First, it takes an image, then by skin color segmentation, it detects human skin color, then it detect human face. How Does It Work? Skin Color Segmentation For skin color segmentation, first we contrast the image. Then, we have to find the largest connected region. Face Detection For face detection, first we convert binary image from RGB image. Then, we try to find the forehead from the binary image. In the figure, X will be equal to the maximum width of the forehead. Eyes Detection For eyes detection, we convert the RGB face to the binary face. Then we find the starting high or upper position of the two eyebrows by searching vertical. Lip Detection For lip detection, we determine the lip box. So, for detection eyes and lip, we only need to convert binary image from RGB image and some searching among the binary image. Apply Bezier Curve on Lip In the lip box, there is lip and may be some part of nose. History
PyGaze | projects Welcome to the PyGaze projects page! Here you will find all sorts of information, source code, and demonstrations of the stuff that we're currently working on at PyGaze HQ. We love to share our interest in science and technology with you! Most of this stuff is closely related to PyGaze, and gives a good idea of how you can use the toolbox. current projects PyGazeAnalyser Analysis of eye-tracking data without having to buy an expensive software package, or relying on a commercial party? eye tracker An eye tracker needn't be expensive! mantis shrimp This isn't really a project, but more of an homage to a creature with a truly incredible pair of eyes. news Sun. 2 March 2014 We have added a new project, and it's a big one! Sun. 12 January 2014 Because we can write about whatever we please: an ode to the mantis shrimp!
ITU Gaze Tracker The ITU Gaze Tracker is an open-source eye tracker that aims to provide a low-cost alternative to commercial gaze tracking systems and to make this technology more accessible. It is developed by the Gaze Group at the IT University of Copenhagen and other contributors from the community, with the support of the Communication by Gaze Interaction Association (COGAIN). The eye tracking software is video-based, and any camera equipped with infrared nightvision can be used, such as a videocamera or a webcam. The cameras that have been tested with the system can be found in our forum. We encourage users and developers to test our software with their cameras and provide feedback so we can continue development. The ITU Gaze Tracker is hosted in SourceForge. In order to run the software, uncompress the zip file and double click on GazeTrackerUI.exe. The user's guide to run and configure the ITU Gaze Tracker can be downloaded from here (PDF document) The requirements to run the ITU Gaze Tracker are:
Motion Detection Algorithms Introduction There are many approaches for motion detection in a continuous video stream. All of them are based on comparing of the current video frame with one from the previous frames or with something that we'll call background. In this article, I'll try to describe some of the most common approaches. In description of these algorithms I'll use the AForge.NET framework, which is described in some other articles on Code Project: [1], [2]. So, if you are common with it, it will only help. The demo application supports the following types of video sources: AVI files (using Video for Windows, interop library is included); updating JPEG from internet cameras; MJPEG (motion JPEG) streams from different internet cameras; local capture device (USB cameras or other capture devices, DirectShow interop library is included). Algorithms One of the most common approaches is to compare the current frame with the previous one. The simplest motion detector is ready! Here is the result of it: Conclusion
Weekend Project: Take a Tour of Open Source Eye-Tracking Software Right this very second, you are looking at a Web browser. At least, those are the odds. But while that's mildly interesting to me, detailed data on where users look (and for how long) is mission-critical. The categories mentioned above do a fairly clean job of dividing up the eye-tracking projects. For example, there are eye-tracking projects designed to work with standard, run-of-the-mill Web cams (like those that come conveniently attached to the top edge of so many laptops), and those meant to be used with a specialty, head-mounted apparatus. Many projects have a particular use-case in mind, but with the ready availability of Webcams, developers are exploring alternative uses suitable for gaming, gesture-input, and all sorts of crazy ideas. Tracking Eye Movement With a Webcam On the inexpensive end of the hardware spectrum are those projects that implement eye-tracking using a standard-issue Webcam. OpenGazer is by far the simplest such project to get started with. Looking Ahead
Eye tracking Measuring the point of gaze or motion of an eye relative to the head Edmund Huey[2] built an early eye tracker, using a sort of contact lens with a hole for the pupil. The lens was connected to an aluminum pointer that moved in response to the movement of the eye. Huey studied and quantified regressions (only a small proportion of saccades are regressions), and he showed that some words in a sentence are not fixated. The first non-intrusive eye-trackers were built by Guy Thomas Buswell in Chicago, using beams of light that were reflected on the eye, then recording on film. In the 1950s, Alfred L. All the records ... show conclusively that the character of the eye movement is either completely independent of or only very slightly dependent on the material of the picture and how it was made, provided that it is flat or nearly flat Records of eye movements show that the observer's attention is usually held only by certain elements of the picture.... Eye-attached tracking [edit]
- Advanced Source Code . Com - Speech Emotion Recognition System .: Click here to download :. Speech emotion recognition is one of the latest challenges in speech processing. Besides human facial expressions speech has proven as one of the most promising modalities for the automatic recognition of human emotions. We have developed a fast and optimized algorithm for speech emotion recognition based on Neural Networks. Index Terms: Matlab, source, code, speech, emotion, recognition, human, computer, interaction. The authors have no relationship or partnership with The Mathworks. Eye Trackers - COGAIN: Communication by Gaze Interaction (hosted by the COGAIN Association) From COGAIN: Communication by Gaze Interaction (hosted by the COGAIN Association) A catalogue of currently available eye trackers, categorized into systems for assistive technology, research purposes etc. Eye Trackers for Assistive Technology and AAC Commercial eye tracking systems that are used for controlling a computer or as communication aids by people with disabilities. Eyetrackers for eye movement research, analysis and evaluation AmTech GmbH, Compact Intergrated Pupillograph (CIP), Pupillograhic Sleepiness Test (PST), table mounted, monocular, video based systems Applied Science Laboratories, ASL, eye tracking and pupillometry systems, both IROG (limbus tracker) and VOG (video) based systems, both head mounted and remote tracking, also mobile tracking! Open source gaze tracking and freeware eye tracking This list contains low-cost, free and open source eye tracking systems and research prototypes, and information that should help in building your own eye tracker. See also
(2) Human Emotion Recognition System | Ali Murad Human Emotion Recognition System Copyright © 2012 MECS I.J. Image, Graphics and Signal Processing, present that day [3]. uences someone‘s behavior. well known and is in many cases visible to a person himself or to the outside world. Fig. 2: Different Human emotions In spite of the difficulty of precisely defining it, emotion is omnipresent and an important factor in human life. of communicating, but also their acting and productivity. Research efforts in human computer interaction are focused on the means to empower computers (robots and other machines) to understand human intention, e.g. speech recognition and gesture recognition systems [1]. computer interaction that could effectively use the capability to understand emotion [2], [3]. role in ‗intelligent room‘ [5] and ‗affective computer tutor‘ [6]. number compared with the efforts being made towards intention-translation means, some researchers are trying to realise man machine interfaces with an emotion understanding capability.
Jim Loy's Three Triangle Puzzle Following on from thinking about non-right integer triangles (they could really do with a catchier name) I came across Jim Loy's Three Triangle Puzzle. He asks, what do these three triangles have in common, beside a side of seven? He mentions one of Euclid's theorems to help us. So, if the length is the same, the opposite angle is the same if it fits inside a circle! I had to look at this a bit more: The biggest equilateral triangle is seven long. Here it is without the arcs: I'm impressed by the neatness of this- all the straight lines are integer lengths - and I notice there are quite a lot of equilateral triangles in here. All this was with the 7-7-7 triangle and its cousins. The same sort of thing can be done with other families of integer triangles. The tall one is the 8-8-4 triangle.
Pythagoras tree (fractal) The Pythagoras tree. The Pythagoras tree with an angle of 25 degrees and smooth coloring. Iteration n in the construction adds 2n squares of size (½√2)n, for a total area of 1. Thus the area of the tree might seem to grow without bound in the limit as n → ∞. It can be shown easily that the area A of the Pythagoras tree must be in the range 5 < A < 18, which can be narrowed down further with extra effort. An interesting set of variations can be constructed by maintaining an isosceles triangle but changing the base angle (90 degrees for the standard Pythagoras tree). In the limit where the half-angle is 90 degrees, there is obviously no overlap, and the total area is twice the area of the base square. Pythagoras tree was first constructed by Albert E. Lévy C curve