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Gabor Wavelets and Morphological Shared Weighted Neural Network Based Automatic Face Recognition

Author(s): Chandrappa D N | Ravishankar M

Journal: Signal & Image Processing
ISSN 2229-3922

Volume: 4;
Issue: 4;
Start page: 61;
Date: 2013;
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Keywords: Face detection | Face recognition | Feed Forward Neural Network (FFNN) | Gabor Wavelets | MSNN

Automatic face recognition system is an important component of intelligent human computer interactionsystems for biometric. It is a attractive biometric approach, to distinguish one person from another. Toperform Automatic face recognition system, the hybrid approach called Gabor Wavelets face detection andMorphological Shared Weighted Neural Network based Face Recognition. Face detection is performed byusing Gabor filter feature extraction. The feature vector based on Gabor filters is used to reduce featuresubspace. The detected face regions are given as input to Morphological Shared-weight Neural Network(MSNN) which performs face recognition. Being non linear and translation invariant, the MSNN can createbetter generalization during face recognition. Feature extraction for MSNN is performed on hit-misstransforms that are independent of gray-level shifts. Then the output is learned by interacting with theclassification process. The system is experimented on standard datasets and also on our own dataset ofimage owing to different illumination conditions and cluttered background in non frontal images with acrowded scene with different conditions. Face detection is performed on a cluttered background andcrowded scene where a false negative and false positive is detected. The MSNN recognize ignores all falsepositives and false negatives from face detection and performs human faces in unconstrained environments,and multi-view recognition.
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