An Enhanced Technique for Face Recognition and Retrieval with Feature Extraction Using Euclidean Distance Classifier
Abstract
Today, image processing enters into various fields, but still it is struggling in recognition issues. Face detection and recognition developed into a very active research area specializing on how to extract and recognize faces within images. Face recognition and retrieval is a widely used biometric application for security and identification concern. The various methods have been proposed for face recognition and each method has advantages and drawbacks. The complexity in process and other issues affects performance of existing system makes insufficient. In this paper presents face recognition and retrieval with geometrical feature vector to calculate the threshold value separately and stored in feature database. The feature is generated and matching is done by Euclidean distance classifier is used to measures a distance between two images. The experimental result shows that block truncation coding method provides better recognition rate when compared with the existing methods such as Local Binary Pattern, Multi-Block Local Binary Pattern Method.
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References
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