ICTACT Journal on Image and Video Processing

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Machine Learning based Artificial Neural Networks for Fingerprint Recognition


Affiliations
1 Department of Electronics and Communication Engineering, Global Academy of Technology, India
     

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Fingerprint identification relies on computations and classification models based on images to identify individuals at their most basic level. For feature extraction, several image preprocessing approaches are used, and image locality bifurcations of different kinds are used for classification. For feature extraction and classification, artificial neural networks (ANNs) are proposed. ANN machine learning method and Gabor filter are introduced in this paper for feature extraction and classification respectively. Artificial Neural Networks and Gabor filtering features are used to create the feature vector. An algorithm based on the extracted features was developed to create a multiclass classifier. Special Database - NIST SD4 served as the basis for evaluation in this research. The Error matrix led to the discovery that, in terms of accuracy, the approach was superior to many traditional machine learning algorithms like Support Vector Machine, Random Forest, Decision Tree and KNN.

Keywords

Artificial Neural Network (ANN), Gabor Filter, Machine Learning, Feature Extraction, Classifiers.Artificial Neural Network (ANN), Gabor Filter, Machine Learning, Feature Extraction, Classifiers.
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  • Machine Learning based Artificial Neural Networks for Fingerprint Recognition

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Authors

N. R. Pradeep
Department of Electronics and Communication Engineering, Global Academy of Technology, India
J. Ravi
Department of Electronics and Communication Engineering, Global Academy of Technology, India

Abstract


Fingerprint identification relies on computations and classification models based on images to identify individuals at their most basic level. For feature extraction, several image preprocessing approaches are used, and image locality bifurcations of different kinds are used for classification. For feature extraction and classification, artificial neural networks (ANNs) are proposed. ANN machine learning method and Gabor filter are introduced in this paper for feature extraction and classification respectively. Artificial Neural Networks and Gabor filtering features are used to create the feature vector. An algorithm based on the extracted features was developed to create a multiclass classifier. Special Database - NIST SD4 served as the basis for evaluation in this research. The Error matrix led to the discovery that, in terms of accuracy, the approach was superior to many traditional machine learning algorithms like Support Vector Machine, Random Forest, Decision Tree and KNN.

Keywords


Artificial Neural Network (ANN), Gabor Filter, Machine Learning, Feature Extraction, Classifiers.Artificial Neural Network (ANN), Gabor Filter, Machine Learning, Feature Extraction, Classifiers.

References