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computer science crazy
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21-02-2009, 10:41 PM

Faces represent complex, multidimensional, meaningful visual stimuli
and developing a computational model for face recognition is difficult
[43]. We present a hybrid neural network solution which compares
favorably with other methods. The system combines local image sampling,
a self-organizing map neural network, and a convolutional neural
network. The self-organizing map provides a quantization of the image
samples into a topological space where inputs that are nearby in the
original space are also nearby in the output space, thereby providing
dimensionality reduction and invariance to minor changes in the image
sample, and the convolutional neural network provides for partial
invariance to translation, rotation, scale, and deformation.
convolutional network extracts successively larger features in a
hierarchical set of layers. We present results using the
Karhunen-Lo`eve transform in place of the self-organizing map, and a
multi-layer perceptron in place of the convolutional network. The
Karhunen-Lo`eve transform performs almost as well (5.3% error versus
3.8%). The multi-layer perceptron performs very poorly (40% error
versus 3.8%). The method is capable of rapid classification, requires
only fast, approximate normalization and preprocessing, and
consistently exhibits better classification performance than the
eigenfaces approach [43] on the database considered as the number of
images per person in the training database is varied from 1 to 5. With
5 images per person the proposed method and eigenfaces result in 3.8%
and 10.5% error respectively. The recognizer provides a measure of
confidence in its output and classification error approaches zero when
rejecting as few as 10% of the examples. We use a database of 400
images of 40 individuals which contains quite a high degree of
variability in expression, pose, and facial details. We analyze
computational complexity and discuss how new classes could be added to
the trained recognizer.
Use Search at http://topicideas.net/search.php wisely To Get Information About Project Topic and Seminar ideas with report/source code along pdf and ppt presenaion
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Joined: Jan 2011
25-01-2011, 04:44 PM

i need materials about face recognition for seminar and presentation.plz send it to me or reply how i can get it?
summer project pal
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Joined: Jan 2011
29-01-2011, 08:25 PM

visit this thread for facial recognition using neural networks:
topicideashow-to-face-recognition-using-neural-networks-download-seminar and presentation-report?page=2
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30-01-2011, 05:05 PM

I required seminar and presentation project and implimentation for face recognition technolgy; Face Gender Recognition in Humans
seminar surveyer
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01-02-2011, 03:00 PM

please go through the following thread for more details on FACE RECOGNITION .

topicideashow-to-face-recognition-technology-a-seminar and presentation-report
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17-01-2012, 07:49 PM

hii..plzz send me the complete ppt with document on face recognition using neural network...
seminar addict
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18-01-2012, 10:23 AM

to get information about the topic FACE RECOGNITION USING Neural Network full report ,ppt and related topic refer the link bellow


topicideashow-to-face-recognition-using-neural-networks-download-seminar and presentation-report

topicideashow-to-face-recognition-using-neural-networks-download-seminar and presentation-report?page=5



study tips
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17-05-2013, 04:11 PM


.ppt   FACE RECONGNITION.ppt (Size: 2.51 MB / Downloads: 15)

What are Neural Network?

A Neural Network is a system of programs and data structures that approximates the operation of the human brain.
A neural network usually involves a large number of processors operating in parallel, each with its own small sphere of knowledge and access to data in its local memory.
Typically, a neural network is initially "trained" or fed large amounts of data and rules about data relationships (for example, "A grandfather is older than a person's father").
A program can then tell the network how to behave in response to an external stimulus or can initiate activity on its own.

Neural Network Architecture

Single layer feed forward network.
Multilayer Feedforward Network
Self Organizing Map(Unsupervised Learning)
Recurrent Network

Disadvantages of PCA

Problems with Eigenfaces (PCA)
Different illumination
Different alignment
Different facial expression

Steps For Face Recognition Using LDA-NN

Square images with W=H=N
M is the number of images in the database
P is the number of persons in the database


To implement the concept of Neural Networks for the purpose of Face Recognition.
Further Recognition of unclear images by removing the background noise.
To improve the accuracy of Face recognition by reducing the number of false rejection and false acceptance errors.
To use Face Thermogram that is output of an infrared camera to detect the faces in dark environments.
Recognition of images captured while in motion.
Recognition of faces in videos (motion picture).

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