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DOI 10.4010/2016.1483
ISSN 2321 3361 © 2016 IJESC
Research Article
Volume 6 Issue No. 5
Desktop Partner
Purnima Bharti1, Yamini Kumari2, Nupur3, Yogita Mevada4
SPPU & SIT, Lonavala, India
Abstract:
The Chat-bot is built using an artificial intelligence algorithm. The bot chats with you as a real person with entertaining replies
which doesn’t make the user know he is really talking to a bot. In this paper, we present a new approach for building desktop
application for chat-bot using text and gestures. We generate a limited database or dictionary which is further to recognize the
keywords using NLP. Our application is not only recognize text or keywords but also recognize the mood of a user through
camera. For example, the user is feeling sad, then system will automatically fetch a joke from database and send it to the user. The
system is able to make a conversation through chatting application. System is able to send some links, web pages or information
by recognizing response from user. For this whole system, we are using technologies like Machine learning, AI, and Data mining.
Keywords: Blob Analysis, Data Mining, Feature Extraction, Haar Classifier, Image processing
I. INTRODUCTION
A desktop partner bot who chats with you when you are bored.
Chat-bots talk in almost every major language. Their language
(Natural Language Processing, NLP) skills vary from
extremely poor to very clever intelligent, helpful and funny.
Chat with you as well as provide information. Need to
recognize the mood of the person and send some jokes if user
is sad. Need to provide suggestion as per user’s personal
interest. The system will be used on personal level, as a
personal desktop which will recognize the face and mood of
user and perform the actions accordingly. The main goal of
this paper is to present a real time system to detect and classify
user mood and sentiments applying blob analysis, Haar
Classifier, extract eye portion etc. The image processing
techniques that we employ are concept detection, near–
duplicate detection, image quality assessment, image
clustering, and face detection. We apply the approach in using
four pre-trained Haar Cascades detectors (frontal face alt,
frontal face alt tree, frontal face alt2, frontal face default)
publicly provided by OpenCV.
trained from a lot of positive and negative images. It is then
used to detect objects in other images.
Fig1. HARCASCADE FEATURE
V. OPENCV HAAR CLASSIFIER
OpenCV provides Harcascade classifier which is used to
detect faces. It provides easy face detection and face regions
and other body parts tracking. Haar classifier detects face
regions in form of rectangular frames. Value of a Haar feature
is difference between the additions of the black and white
rectangular frames pixel values.
II. BACKGROUND SUBTRACTION METHOD
Background subtraction (BS) calculates the foreground mask
performing a subtraction between the current frame and a
background model, containing the static part of the scene or,
more in general, everything that can be considered as
background given the characteristics of the observed scene.
III. BLOB ANALYSIS
Blob Analysis is a fundamental technique of machine vision
based on analysis of consistent image regions. As such it is a
tool of choice for applications in which the objects being
inspected are clearly discernible from the background. Diverse
set of Blob Analysis methods allows to create tailored
solutions for a wide range of visual inspection problems.
Fig.2.HAAR-CLASSIFIER
IV. FACE DETECTION
The FACE Detection using Haar feature-based cascade
classifiers is an effective object detection method. It is a
machine learning based approach where a cascade function is
International Journal of Engineering Science and Computing, May 2016
Haar-Classifier detects face regions in form of rectangular
frames. Value of a Haar feature is difference between the
additions of the black and white rectangular frames pixel
values.
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VI. SYSTEM ARCHITECTURE
[4] O. Langner, R. Dotsch, G. Bijlstra, D. Wigboldus, S.
Hawk, and A. Knippenberg. Presentation and validation of the
Radboud Faces Database. Cognition and Emotion 24 (8),
13771388, 2010.
[5] E. Simo-Serra, A. Quattoni, C. Torras, and F. MorenoNoguer.A Joint Model for 2D and 3D Pose Estimation from a
Single Image. In IEEE CVPR, 2013
Fig.3. SYSTEM ARCHITECTURE
In our system, firstly we will be using OpenCV Technology to
capture continuous images of the driver. By using the
algorithms viz. Blob analysis, Feature extraction, Yawning
Detection Algorithm, analysis of the expressions on the
driver’s face is done. In the analysis, the stress level on the
driver’s face is determined. Based on the analysis, if the stress
level is more than alert messages are sent to the emergency
contact numbers of the driver and to the driver himself. In case
of any accident scenario, if the driver says a word “Help”,
alert messages are sent again to his emergency contact
numbers.
VII. CONCLUSION AND FUTURE SCOPE
Conclusion: Many times while using pc/laptop people get
bored and sometimes they need someone to talk to so we are
creating artificial desktop partner which uses facial detection,
mood-detection, sentiments detection and various other
techniques to detect users emotion and can chat like real
human or can play song or can fetch jokes as per the situation.
It learns user’s behavior and using training set can send
various links to user.
Future Scope: The system will be used on personal level, as a
Personal desktop which will recognize the face and mood of
the user and perform the actions accordingly.
VIII.REFRENCES
[1] O. Langner, R. Dotsch, G. Bijlstra, D. Wigboldus, S.
Hawk, and A. Knippenberg. Presentation and validation
of the Radboud Faces Database. Cognition and Emotion
24 (8), 1377–1388, 2010.
[2] E. Simo-Serra, A. Quattoni, C. Torras, and F. MorenoNoguer. A Joint Model for 2D and 3D Pose Estimation
from a Single Image. In IEEE CVPR, 2013
[3] Shirin Shokrani, Payman Moallem, Mehdi Habibi”
Facial Emotion Recognition Method Based on Pyramid
Histogram of Oriented Gradient over Three Direction of
Head”. In ICCKE,2014
International Journal of Engineering Science and Computing, May 2016
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