Study of Ear Biometrics Based Identification System Using Machine Learning

Within the biometric industry, computerized person identification using ear pictures is a hot topic. The ear, like other biometrics like the face, iris, and fingerprints, contains a huge number of particular and unique traits that may be used to identify a person. Due to the mask-wearing scenario, most face detection methods fail in this present international COVID-19 pandemic. The eardrum is a great data source for inactive person authentication since it doesn't necessitate the person we're attempting to pinpoint to cooperate, and the structure of the ear doesn't change significantly over time.. The acquisition of a human ear is also simple because the ear is apparent even while wearing a mask. An ear biometric system can enhance other biometric technology in an automated person identification system by giving authentication cues when other information is unreliable or even missing. We provide a six-layer deep convolutional architecture for ear identification in this paper. On the IITD ear dataset, the deep network's potential efficiency is assessed. The IITD has a detection performance of 97.36 percent for the deep network model and 96.99 percent for the IITD. When paired with a competent surveillance system, this approach can be beneficial in identifying people in a large crowd. Keywords: Biometrics, Person identification, IIT-D, Deep learning, Ear dataset

EAR BIOMETRICS: ONE MORE STEP TOWARDS SOCIAL DISTANCING

Biometrics deals with identification of individuals based on their characteristics. In traditional systems people use to remember passwords, PIN's, tokens for identification. But in recent years biometrics have gained a lot of popularity due to advancement in technology and the secure environment. Variety of biometrics have been developed in recent years such as fingerprint, iris, retinal. Ears are considered as the future of biometrics due to its properties of Universality, which means that every person should have it. Uniqueness, which indicates that no two persons should behave the same characteristic. Permanence, which means that this characteristic does not change over time. Collectability, which indicates that the characteristic can be measured quantitatively. Ears are comparatively large in size and hence are more visible and also their shape does not change radically over a period of time and so are their images easy to be captured.