There are 3 state of the art approaches for recognizing faces under varying poses. These 3 approaches are Overlapping
Discrete Cosine Transform (DCT), Hybrid Spatial Feature Interdependence Matrix (HSFIM) and Score Level Fusion
Techniques (SLFT). The train and test images are considered from standard public face data bases: Head Pose Image
Database, Sheffield Face Database, and Indian Face Database. The key contribution of this article is, we have developed
and analyzed the 3 state of the art approaches for recognizing faces under varying poses using a common set of train and
test images. This evaluation gives us the exact face recognition rates of the 3 systems under varying poses. We have considered
patents ‘Face recognition apparatus, face recognition method, gabor filter application apparatus, and computer
program’ ‘Gabor filtering and joint sparsity model-based face recognition method’ ‘Face identification method based on
multiscale weber local descriptor and kernel group sparse representation’.
Keywords: Discrete cosine transform, face recognition, hybrid spatial feature interdependence matrix, score level fusion.
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