Face Recognition for Surveillance Purpose
Using Orthogonal Transforms and Vector Quantization Techniques
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This book is an attempt to unravel the problem of human face recognition. Face recognition is a biometric authentication method that has become more and more relevant in the recent years for the purpose of surveillance. Face recognition is a popular research area where there are different approaches studied in the literature. In this book face recognition problem is handled by applying Principal Component Analysis (PCA), Various Orthogonal transforms and different Vector Quantization (VQ) codebook generation techniques. The Eigen face method tries to find a lower dimensional space for the repr...
This book is an attempt to unravel the problem of human face recognition. Face recognition is a biometric authentication method that has become more and more relevant in the recent years for the purpose of surveillance. Face recognition is a popular research area where there are different approaches studied in the literature. In this book face recognition problem is handled by applying Principal Component Analysis (PCA), Various Orthogonal transforms and different Vector Quantization (VQ) codebook generation techniques. The Eigen face method tries to find a lower dimensional space for the representation of the face images. The main drawback of PCA is scalability. As dataset changes the whole eigenspace distribution also changes. To avoid this difficulty various orthogonal transforms like DCT, DST, WHT, Slant, Wavelet Transform and newly proposed Kekre's Transform are applied on database. The concept of image energy compaction in low frequency coefficients is explored here.The VQ is considered to be a good data compression method. The key to VQ is the good codebook generation. Here the new technique for codebook generation is introduced as Kekre's Fast Code Book Generation (KFCG).
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