FEATURE RECOGNITION: A CONTEMPORARY SURVEY OF GROWING NEEDS BLENDED WITH MACHINE LEARNING FOR SURVEILLANCE, SECURITY AND INTELLIGENCE SYSTEMSJournal: International Journal of Advanced Research in Engineering and Technology (IJARET) (Vol.11, No. 12)
Publication Date: 2020-12-31
Authors : M. Swetha;
Page : 1813-1829
Keywords : Feature Detection; Extraction; PCA; Eigenvectors; Eigen Features.;
Feature recognition is one of the most prominent areas of Machine Learning (ML) since ages together human are fascinated with the world of colors, features, virtues, and such artifacts for gaining inside into the knowledge sharing. The intuitiveness of the human mind is quite capable of recognizing and pursing miniature details about the surroundings and the environment where they live in. In so far, even the animals and other living beings also have the power to recognize and assimilate the information─ however, they lack in understanding, interpolation and other feature extraction details. The traditional AI has been used for various applications starting from feature detection, extraction and applying convolution techniques for feature engineering. In achieving so Principal Component Analysis (PCA) gives a mathematical model to get inside with reduction feature extraction and computational efficiency. This image processing area has being fascination for humans to see nice features and increase their happiness quotient. On the other side, the people are playing the game to reshape the feature and get a noticed like criminals, terrorists, fraudulent and other such irrelevant human behavior. However some of the features never changes through the life span of like retina, lines or regions on the hand, thumb impression etc., This salient features are going to be retain throughout the life journey and everlasting. In this article an attempt is being made to use ML algorithms for such areas and with pertinent mathematical background; thereby we can understand certain artifacts of features and use them for a good cause.
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