Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
COSMOS-A Representation Scheme for 3D Free-Form Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Line Pattern Retrieval Using Relational Histograms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition using Shading-Based Curvature Attributes
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Face Recognition Based on DCT and 2DLDA
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
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This paper presents, efficient transform based face recognition technique which considers full and partial feature vector of an image. 2D-DCT and Walsh transform is applied on the resized image of size 128x128, to obtain its feature vector. Partial feature vector is obtained by selecting 75% rows and columns of feature vector, 50% rows and columns of feature vector and so on. The smallest size of partial feature vector is selected as 4x4. Proposed technique is tested on two different databases. Georgia Tech Face Database contains JPEG color images and Indian Face Database contains Bitmap color images of varying size. Recognition rate is calculated for varying size of selected feature vector using DCT and Walsh transform and compared. Also computational complexity in terms of number of CPU units is compared in both the cases: with full feature vector and with partial feature vector. Results show that, Walsh transform gives better recognition rate than DCT and number of CPU units required using 2D- Walsh transform is almost 9 times less than that of required by using 2D-DCT. This is because the multiplications required in Walsh transform are zero.