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Share  Title: Gender Classification Using Support Vector Machines (SVMs)

 

 
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Automatic face recognition is a classic problem in the area of computer vision research. This problem is still a very active area of research in vision community. The primary reason for this problem to get so much attention is the fact that face recognition finds application in many commercial applications and can work as a biometric in many law enforcement applications. The problem of Automatic face recognition can be formally defined as follows: Given a set of representative training images for each person in the database, determine the identity of a new face images from the stored data. There have been several techniques proposed in literature to extract different type of features related to shape, color, etc. of the face. Some of the techniques simply use the image pixel values as the features and reduce the dimension of these features by applying some constraints such that classification property of training image is preserved. In Project 1 of this course, we examined different kind of feature reduction method and used nearest neighbor approach as the classifier. The fundamental problem with the use of nearest neighbored approach as classifier is that the probability of error is quite high (twice as compared with Bayes classification). Hence, we explore another classifier known as Support Vector Machines (SVMs) which has gained significant attention in recent years. To test the performance of SVMs, we have reformulated the problem of face recognition into a gender classification problem. The problem can be stated as follows: given a test image, determine its gender.

 
 
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Keywords

vector  machines  gender  classification  
 
 
 About This Document
 
 Subject Computer Science
 Category Term Paper
 Views 3344
 Downloads 335
 Added 25-03-10
 Contributor   ravigarg
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