title,doi,url,abstract,journal,publication_year,pmid,arxiv
Ear Recognition using Scale Invarient Feature Transform (SIFT), https://doi.org/10.32628/IJSRCSEIT, https://ijsrcseit.com/CSEIT172538, Biometric-based solutions are able to provide for confidential financial transactions and personal data privacy. The need for biometrics can be found in federal state and local governments in the military and in commercial applications. Enterprise-wide network security infrastructures government IDs secure electronic banking investing and other financial transactions retail sales law enforcement and health and social services are already benefiting from these technologies.

There exist few techniques in the literature which can be used to detect ear auto-matically. A detailed review of these techniques is as follows. The first well known technique for ear detection is due to Burge and Burger [1]. It has detected ears with the help of deformable contours. But contour initialization in this technique needs user interaction. As a result ear localization is not fully automatic. Hurley et al. [2] have used force field technique to get the ear location. The technique claims that it does not require exact ear localization for ear recognition. However it is only applicable when a small background is present in ear image. In [3] Yan and Bowyer have used manual technique based on two-line landmark to detect ear where one line is taken along the border between the ear and the face while other line is considered from the top of the ear to the bottom. The 2D ear localization technique proposed by Alvarez et al. [4] uses ovoid and active contour (snake) [5] models. Ear boundary is estimated by fitting the contour of an ear in the image by combining snake and ovoid models. This technique requires an initial approximated ear contour to execute and hence cannot be used in fully automated ear recognition system. There is no empirical evaluation of the technique.
, International Journal of Scientific Research in Computer Science Engineering and Information Technology, 2017, CSEIT172538