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Human Face Recognition Using PCA on Wavelet Subband


Posted by: seminar class
Created at: Thursday 05th of May 2011 11:45:18 PM
Last Edited Or Replied at :Thursday 05th of May 2011 11:45:18 PM
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gnition, Principal Component Analysis, Wavelet transform, Subband.
1. Introduction
Over the past 20 years, extensive research works on various aspects of face recognition by human
and machines have been conducted by psychophysicists, neuroscientists and
engineering scientists. Psychophysicists and neuroscientists have studied issues such as uniqueness
of faces, how infants perceive faces and organization of memory of faces. While engineering
scientists have designed and developed face recognition algorithms. This paper continues the work
done by engineering sci..................[:=> Show Contents <=:]



HIGH-SPEED FACE RECOGNITION ppt


Posted by: seminar surveyer
Created at: Monday 24th of January 2011 02:59:50 AM
Last Edited Or Replied at :Thursday 19th of January 2012 10:33:25 PM
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lied in the truncated DCT domain .

CLASSIFICATION USING RBF NN
Width estimation
Weight adjustment

CONCLUSION

To maintain the main facial features.
Data independency fast computational speed.
Effect of non-uniform illumination can be alleviated.


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FACE RECOGNITION TECHNOLOGY


Posted by: [email protected]
Created at: Sunday 14th of March 2010 09:25:15 PM
Last Edited Or Replied at :Thursday 18th of March 2010 07:55:40 AM
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i need hole document for the
FACE RECOG..................[:=> Show Contents <=:]



FACE RECOGNITION USING Neural Network


Posted by: SHARMILAA
Created at: Tuesday 16th of February 2010 01:13:13 AM
Last Edited Or Replied at :Monday 06th of February 2012 01:32:26 AM
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FULL REPORT..................[:=> Show Contents <=:]



Artificial Inteligence for speech recognition


Posted by: yogamaya das
Created at: Sunday 24th of January 2010 09:25:19 AM
Last Edited Or Replied at :Sunday 24th of January 2010 12:02:19 PM
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can u plz send d docu..................[:=> Show Contents <=:]



3D Face Recognition


Posted by: electronics seminars
Created at: Monday 30th of November 2009 03:38:04 AM
Last Edited Or Replied at :Monday 03rd of January 2011 11:16:25 PM
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resented for constructing a database of 3D face models and matching this database to 2.5D face scans which are captured from different views. 2.5D is a simplified 3D (x, y, z) surface representation that contains at most one depth value (z direction) for every point in the (x, y) plane. A robust similarity metric is defined for matching. To address the non-rigid facial movement, such as expressions, we present a facial surface modeling and matching scheme to match 2.5D test scans in the presence of both non-rigid deformations and large pose changes (multiview) to a neutral expression 3D face m..................[:=> Show Contents <=:]



Face Recognition using the Techniques Base on Principal Component Analysis PCA


Posted by: computer science crazy
Created at: Monday 23rd of February 2009 12:51:20 PM
Last Edited Or Replied at :Monday 04th of March 2013 01:51:53 AM
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ntly in use, face recognition has its own application. Principal Component Analysis (PCA) has been variously used in pattern recognition applications, especially in face recognition field. In this seminars, several techniques based on standard PCA, Fisher Linear Discriminant (FLD), Face Specific Subspace (FSS) will be discussed. We will review the developed idea used in the 2D-PCA method and its application on the FSS approach. For the experimental results, several tests have been performed over on three face image databases: ORL, Yale, and UMIST; The approaches will be illustrated using a web..................[:=> Show Contents <=:]



FACE RECOGNITION USING Neural Network


Posted by: computer science crazy
Created at: Saturday 21st of February 2009 11:11:21 AM
Last Edited Or Replied at :Monday 06th of February 2012 01:32:50 AM
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atures in a
hierarchical set of layers. We present results using the
Karhunen-Loeve transform in place of the self-organizing map, and a
multi-layer perceptron in place of the convolutional network. The
Karhunen-Loeve 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 on the database considered as th..................[:=> Show Contents <=:]



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