Face and Gender Recognition Using Principal Component Analysis
Journal Title: International Journal on Computer Science and Engineering - Year 2010, Vol 2, Issue 4
Abstract
Face recognition is a biometric analysis tool that has enabled surveillance systems to detect humans and recognize humans without their co-operation. In this paper we evaluate the basics of the Principal Component Analysis (PCA) and verify the results of this algorithm on a training database of images. The same principle is in effect used to recognise the gender of the test image by evaluating the Euclidian distance of the test mage from the images in the database. The proposed gender and face recognition technique using PCA is verified for both est images of a man and a woman. It was observed that if the number of images of a particular subject was more in the atabase, the gender recognition becomes even better. The effect of salt and pepper noise and image cropping was also observed and the results hold true for noise up to 40 percent of the image pixels.
Authors and Affiliations
Dr. H. B. Kekre , Sudeep D. Thepade , Tejas Chopra
Keywords
Related Articles
- EP ID EP119018
- DOI -
- Views 112
- Downloads 0
A Effective and Complete Preprocessing for Web Usage Mining
Now, peoples are interested in analyzing log files which can offer valuable insight into web site usage. The log files hows actual usage of web site under all circumstances and don’t need to conduct external experimenta...
Dual Moving Text Line Detection and Selective Masking
In this paper we are proposing a technique to extract a moving text-line in a video and split if there are dual moving text-line in it. In second stage we mask the one or both moving text-line to secure the information o...
Supply and demand vs. price driven graphs financial data visualization
In the financial world, people almost every day, argue whether the price of a financial instrument will go up or down. While long term investors use fundamental data to create their view, mid & short term investors a...
An Efficient Pruning Technique for Mining Frequent Itemsets in Spatial Databases
Frequent Itemset Mining is evaluating the rules and relationship within the data items are optimizing it, in the large spatial databases (for e.g. Images, Docs, AVI files etc).It is one of the major problems in DM (Data...
AN EFFICIENT TEXT CLASSIFICATION USING KNN AND NAIVE BAYESIAN
The main objective is to propose a text classification based on the features selection and preprocessing thereby reducing the dimensionality of the Feature vector and increase the classification accuracy. Text classifica...