Muzzle-based Cattle Identification using Speed up Robust Feature Approach

Citation:
Ahmed, S., T. Gaber, Alaa Tharwat, and A. E. Hassanien, "Muzzle-based Cattle Identification using Speed up Robust Feature Approach", IEEE International Conference on Intelligent Networking and Collaborative Systems, ,015, pp. 99-104, Taipei, Taiwan, 2-4 September , 2015.

Date Presented:

2-4 September

Abstract:

Starting from the last century, animals identification
became important for several purposes, e.g. tracking,
controlling livestock transaction, and illness control. Invasive and
traditional ways used to achieve such animal identification in
farms or laboratories. To avoid such invasiveness and to get more
accurate identification results, biometric identification methods
have appeared. This paper presents an invariant biometric-based
identification system to identify cattle based on their muzzle
print images. This system makes use of Speeded Up Robust
Feature (SURF) features extraction technique along with with
minimum distance and Support Vector Machine (SVM) classifiers.
The proposed system targets to get best accuracy using minimum
number of SURF interest points, which minimizes the time
needed for the system to complete an accurate identification.
It also compares between the accuracy gained from SURF
features through different classifiers. The experiments run 217
muzzle print images and the experimental results showed that
our proposed approach achieved an excellent identification rate
compared with other previous works.

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