1 Affiliation : Bharati Vidyapeeth’s College of Engineering, New Delhi, India
Email : email@example.com
2 Affiliation : Bharati Vidyapeeth’s College of Engineering, New Delhi, India
Email : Kanikasomani123@gmail.com
3 Affiliation : Bharati Vidyapeeth’s College of Engineering, New Delhi, India
Email : firstname.lastname@example.org
4 Affiliation : Bharati Vidyapeeth’s College of Engineering, New Delhi, India
Email : email@example.com
5 Affiliation : Bharati Vidyapeeth’s College of Engineering, New Delhi, India
Email : Singhs.firstname.lastname@example.org
6 Affiliation : Bharati Vidyapeeth’s College of Engineering, New Delhi, India
Email : email@example.com
In this paper, unique features of the segmented image samples are extracted by using two major feature extraction techniques: Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). After this, these features are fused to get more precise and productive outcomes. The average accuracy of the three distinct datasets that were generated using the LBP and HOG features are determined. To calculate the accuracy of the three distinct models, classification techniques like KNN and SVM, are adopted.
Palm Print Recognition; Region of Interest; Local Binary Pattern; Histogram of Oriented Gradients.
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