Abstract
The suitability of face recognition was investigated as a biometric-based identifier for sheep using a holistic analysis of face images by the independent components technique. Algorithm training was performed independently on several normalized face images from 50 sheep (sets of two, three, and four training images per sheep). The performance of this technique was assessed on a separate set of images (three normalized face images per sheep) using the cosine distance classifier. When 180 to 200 components were extracted, the recognition rate was as high as 95.3% to 96%. As expected, fewer independent components reduced the recognition rate, while a higher number of training images per sheep improved it. Although our results have demonstrated the potential of face recognition as a non-invasive, inexpensive, and accurate novel biometric identifier of sheep, further work should aim at improving recognition rates on a larger set of sheep faces.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
