Robust Face Recognition Algorithm with A Minimum Datasets

Mohammed Ehsan Safi; Eyad  I. Abbas; Ayad  A. lbrahim

doi:10.24237/djes.2021.14211

Authors

Mohammed Ehsan Safi
mohammed.e.safi@uotechnology.edu.iq
Department of Electrical Engineering, University of Technology
Eyad I. Abbas Department of Electrical Engineering, University of Technology
Ayad A. lbrahim Department of Electrical Engineering, University of Technology

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 reduces the training sets in addition to obtaining a high recognition rate.

Conclusion

Provide The biometric modal system proposed in this paper using PCA with the minimum mean of clustering. The proposed algorithm is fast, simple, and robust for the person Recognition process. Moreover, the algorithm has been shown reliability for database reduction. The experiment result has been offered a high recognition rate (100%) despite 44% of database redaction. Nevertheless, the decrease in a recognition rate to 70% with 89% for the database of the training set for testing with the pattern out of the training set, but still a good result in minimizing the training set with respect to recognition rate with corresponding to the application that uses this algorithm. In addition to the recognition rate, 100% for testing with the pattern has the same pose in the training set. 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.

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