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Research On The Technology Of Face Recognition Based On Ageing Variances And Face Reconstruction

Posted on:2009-12-31Degree:MasterType:Thesis
Country:ChinaCandidate:L J FanFull Text:PDF
GTID:2178360275450866Subject:Computer application technology
Abstract/Summary:PDF Full Text Request
Face recognition technology has been attached great importance to the researchers for its scientific significance and practical value in the past few years,and become the hotspot of pattern recognition and artificial intelligence.However,the recognition rate will decline sharply due to the shape and texture change in the face for different age.To solve this problem,we considered both age estimation and face image reconstruction,and bring them into face recognition algorithm which based on the present face recognition and age change research.Thereby we present the face recognition which allows age change.We designed and implemented the prototype system of face recognition.The highlights and main contributions of the dissertation include:(1)An age estimation algorithm based on Local Binary Pattern and Support Vector Machine is proposed.After analyzing the advantages and disadvantages of the existing age estimation method,we extract the key feature from human aging faces using Local Binary Pattern,then use the Principal Component Analysis method to choose the eigenvector,at last we use support vector regression method to train and gain the whole age function which established the corresponding relationship between texture feature and the age.Experimental results show that the method can effectively estimate the age of the human faces which the error is about 4 year,at the same time provide age measurement for face image reconstruction.(2)A human face image reconstruction for different age based on the shape and texture characteristics is presented.First,change face shape according to the facial characteristics data,combined with Radial Base Function deformation technology.We get age texture by decomposing and selecting the elderly face images using the log-gabor wavelet,then add it to the changed face image.During the process,we can reconstruct the target age human face by judging the age of human face through the age estimation function,adjusting age texture parameter which can control wrinkle's changes.Experimental results show that the method can livingly reconstruct different ages of human faces.(3)Classification algorithm is studied which based on discrete cosine transform and the Hidden Markov Model.For the traditional discrete cosine transform contained small amount of information,we use discrete cosine transform which based on sampling to expand the coverage of features,and then using Hidden Markov Model classification algorithm,then came face recognition which allows age changes,and also verified the validity and rationality of face image reconstruction process.(4)A prototype system of face recognition which allows age changes is designed and implemented based on the idea of oriented object.The system consists of four modules:image preproeess,age estimation,face image reconstruction and face recognition.And makes the system recognize people according to face image even the training image is collected 5 years earlier than the test image.
Keywords/Search Tags:face recognition, age estimation, face image reconstruction, discrete cosine transform, Hidden Markov Model
PDF Full Text Request
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