The Effect of Clustering with a minimum Pattern of Features Extraction for Person Recognition

Abstract

In personal image recognition algorithms, two effective factors govern the system’s evaluation, recognition rate and size of the database. Unfortunately, the recognition rate proportional to the increase in training sets. Consequently, that increases the processing time and memory limitation problems. This paper’s main goal was to present a robust algorithm with minimum data sets and a high recognition rate. Images for ten persons were chosen as a database, nine images for each individual as the full version of the training data set, and one image for each person out of the training set as a test pattern before the database reduction procedure. The proposed algorithm integrates Principal Component Analysis (PCA) as a feature extraction technique with the minimum means of clusters and Euclidean Distance to achieve personal recognition. After indexing the training set for each person, the clustering of the differences is determined. The recognition of the person represented by the minimum mean index; this process returned with each reduction. The experimental results show that the recognition rate is 100% despite reducing the training sets to 44%, while the recognition rate decrease to 70% when the reduction reaches 89%. The clear picture out is the results of the proposed system support the idea of the redaction of training sets in addition to obtaining a high recognition rate based on application requirements.

A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks

“…It is based on the K-D tree. The priority level is used to clarify the distance of the queried node, the content is searched in an increasing order, and the closest distance is specified [ 17 , 18 ]. When checking the content of some nodes, the K-D tree search algorithm takes a lot of time, and only some nodes meet the relevant requirements in the final result.…”