An Experimental Study Using Scale Invariant Feature Transform and Key-Point Extraction for Human Ear Recognition System

  • Subhranil SomEmail author
  • Renuka MahajanEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1039)


Abundant research has been done on the improvement of the security and trustworthiness of biometric systems. The aim of this paper is to demonstrate the image key-point extraction technique and establish its uniqueness for biometric identification. Ear features comes out to be one of the important biometric systems, which prove to have great potential, in identifying humans in the real world applications. In this work, key-point based matching and recognition is done using SIFT (Scale Invariant Feature Transform) technique. This approach extracts features from images of distinctive invariant. These images are utilized to perform consistent matching between various objects (ear). The key-points are invariant to image scale and hence can provide good matching over a wide range of images. The distinctive features have been matched correctly using the proposed technique and tested on a large database of ear images. This study helps in establishing that the experimental results show improvements in recognition accuracy.


Scale Invariant Feature Transform (SIFT) Laplacian of Gaussian (LoG) Feature Points (FPs) Difference of Gaussian (DoG) Key Points (KPs) Data set (DS) Match Points (MPs) 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Amity University Uttar PradeshNoidaIndia
  2. 2.Jaipuria Institute of ManagementNoidaIndia

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