July 2022 Issue Vol.12 No.7
Dr. V. K. Narendira Kumar
Assistant Professor of Computer Science, PG & Research Department of Computer Science,
Gobi Arts & Science College (Autonomous), Gobichettipalayam,
Erode District, Tamil Nadu, India.
Dr. S. Prasath
Assistant Professor, School of Computer Science,
VET Institute of Arts and Science (Co-Edu) College, Erode, Tamil Nadu, India.
Abstract: Biometrics is physical or behavior characteristics that can be used for human identification. The features currently used in commercial systems or in research investigations include: fingerprints, face, hand geometry, handwriting, retinal, iris, vein, and voice. As the security starts to play an important role in the daily life, biometric technologies are becoming the solutions to highly secure identification and personal verification. Although features like fingerprints, the face and iris are well understood, researchers are still interested in finding alternative biometrics. Here, researcher propose the ear as a biometric and investigate it with both 2D and 3D data. The work presents results of the largest experimental investigation of ear biometrics to date. The ICP-based algorithm also demonstrates good scalability with size of dataset. These results are encouraging in that they suggest a strong potential for 3D ear shape as a biometric. Multi-biometric 2D and 3D ear recognition are also explored. The proposed automatic ear detection method will integrate with the current system, and the performance will be evaluated with the original one. The investigation of ear recognition under less controlled conditions will focus on the robustness and variability of ear biometrics. Some initial experiments were carried out on the small dataset, but a larger dataset is required to verify the observations and draw strong conclusions. Multi-modal biometrics using 3D ear images will be explored, and the performance will be compared to existing biometrics experimental results.
Keywords: Biometrics, Recognition, Verification, Detection, Eigen Ear, Extraction, Matching, and Datasets.