PPT'S

10 Seminar presentation techniques


 10 Seminar presentation techniques 
Here are ten presentation techniques that can improve your final year seminar or project presentation.

1. Practice is the most important word here, practice your presentation at least 7 times in front of a mirror if you are a first time presenter.

2. Have eye contact with the audience while presenting.

3. While preparing for presentation keep in mind about you the state of your audience.

4. Try including interesting points they like.

5. Provide a few examples related to your topic.

6. Pay attention to your voice. It should keep on modulating depending up on words. Maintaining the same pitch will make it boredom.

7. If you are reading from a paper, make sure that you keep your eye contact with the audience in between.

8. Prepare cue cards. (are cards with words written on them that help actors and speakers remember what they have to say). This helps you to remember your words and confidence.

9. Design your slides intelligently. Don’t put many texts on your slide. Avoid putting unnecessary images that you won’t discuss. Also avoid reading from the slides. Make sure your audience reads your text first before you.

10. Make your presentation a two way communication, ask questions to your audience, be prepared to answer their questions.


IMAGE PROCESSING
                      
FEATURE BASED EIGENFACE METHOD 

FOR FACIAL RECOGNITION 

Digital image processing techniques visually enhances and statistically evaluates some aspect of an image not readily apparent in its original form. Face is the index of the mind, so the recognition of face is an important aspect in various fields. In this paper we discuss about the feature based recognition and eigenface method for facial analysis. In feature based facial recognition method the importance is given to the facial features (eyes, nose, eyebrows, etc.,), whereas the eigenface method gives preference to the face. By combining both the above methods we obtain “Feature Based Eigenface Method” for facial recognition, due to its superiority in its near real time speed and reasonably simple implementation. Using the feature based eigenface method we can develop a better facial recognition systems for Humanoid robots and for crime investigation.

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