Deep Transfer Learning-Based Animal Face Identification Model Empowered with Vision-Based Hybrid Approach
The importance of accurate livestock identification for the success of modern livestock industries cannot be overstated as it is essential for a variety of purposes, including the traceability of animals for food safety, disease control, the prevention of false livestock insurance claims, and breedi...
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| Format: | Article |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/2/1178 |
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| author | Munir Ahmad Sagheer Abbas Areej Fatima Ghassan F. Issa Taher M. Ghazal Muhammad Adnan Khan |
| author_facet | Munir Ahmad Sagheer Abbas Areej Fatima Ghassan F. Issa Taher M. Ghazal Muhammad Adnan Khan |
| author_sort | Munir Ahmad |
| collection | DOAJ |
| description | The importance of accurate livestock identification for the success of modern livestock industries cannot be overstated as it is essential for a variety of purposes, including the traceability of animals for food safety, disease control, the prevention of false livestock insurance claims, and breeding programs. Biometric identification technologies, such as thumbprint recognition, facial feature recognition, and retina pattern recognition, have been traditionally used for human identification but are now being explored for animal identification as well. Muzzle patterns, which are unique to each animal, have shown promising results as a primary biometric feature for identification in recent studies. Muzzle pattern image scanning is a widely used method in biometric identification, but there is a need to improve the efficiency of real-time image capture and identification. This study presents a novel identification approach using a state-of-the-art object detector, Yolo (v7), to automate the identification process. The proposed system consists of three stages: detection of the animal’s face and muzzle, extraction of muzzle pattern features using the SIFT algorithm and identification of the animal using the FLANN algorithm if the extracted features match those previously registered in the system. The Yolo (v7) object detector has mean average precision of 99.5% and 99.7% for face and muzzle point detection, respectively. The proposed system demonstrates the capability to accurately recognize animals using the FLANN algorithm and has the potential to be used for a range of applications, including animal security and health concerns, as well as livestock insurance. In conclusion, this study presents a promising approach for the real-time identification of livestock animals using muzzle patterns via a combination of automated detection and feature extraction algorithms. |
| first_indexed | 2024-03-09T13:40:27Z |
| format | Article |
| id | doaj.art-859f346791684f808b46328230ffa584 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T13:40:27Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-859f346791684f808b46328230ffa5842023-11-30T21:07:17ZengMDPI AGApplied Sciences2076-34172023-01-01132117810.3390/app13021178Deep Transfer Learning-Based Animal Face Identification Model Empowered with Vision-Based Hybrid ApproachMunir Ahmad0Sagheer Abbas1Areej Fatima2Ghassan F. Issa3Taher M. Ghazal4Muhammad Adnan Khan5School of Computer Science, National College of Business Administration & Economics, Lahore 54000, PakistanSchool of Computer Science, National College of Business Administration & Economics, Lahore 54000, PakistanDepartment of Computer Science, Lahore Garrison University, Lahore 54000, PakistanSchool of Information Technology, Skyline University College, University City Sharjah, Sharjah 1797, United Arab EmiratesSchool of Information Technology, Skyline University College, University City Sharjah, Sharjah 1797, United Arab EmiratesDepartment of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam 13120, Republic of KoreaThe importance of accurate livestock identification for the success of modern livestock industries cannot be overstated as it is essential for a variety of purposes, including the traceability of animals for food safety, disease control, the prevention of false livestock insurance claims, and breeding programs. Biometric identification technologies, such as thumbprint recognition, facial feature recognition, and retina pattern recognition, have been traditionally used for human identification but are now being explored for animal identification as well. Muzzle patterns, which are unique to each animal, have shown promising results as a primary biometric feature for identification in recent studies. Muzzle pattern image scanning is a widely used method in biometric identification, but there is a need to improve the efficiency of real-time image capture and identification. This study presents a novel identification approach using a state-of-the-art object detector, Yolo (v7), to automate the identification process. The proposed system consists of three stages: detection of the animal’s face and muzzle, extraction of muzzle pattern features using the SIFT algorithm and identification of the animal using the FLANN algorithm if the extracted features match those previously registered in the system. The Yolo (v7) object detector has mean average precision of 99.5% and 99.7% for face and muzzle point detection, respectively. The proposed system demonstrates the capability to accurately recognize animals using the FLANN algorithm and has the potential to be used for a range of applications, including animal security and health concerns, as well as livestock insurance. In conclusion, this study presents a promising approach for the real-time identification of livestock animals using muzzle patterns via a combination of automated detection and feature extraction algorithms.https://www.mdpi.com/2076-3417/13/2/1178livestock identificationlivestock muzzle pattern identificationhorse identificationautomated horse identificationyoloequine biometrics |
| spellingShingle | Munir Ahmad Sagheer Abbas Areej Fatima Ghassan F. Issa Taher M. Ghazal Muhammad Adnan Khan Deep Transfer Learning-Based Animal Face Identification Model Empowered with Vision-Based Hybrid Approach Applied Sciences livestock identification livestock muzzle pattern identification horse identification automated horse identification yolo equine biometrics |
| title | Deep Transfer Learning-Based Animal Face Identification Model Empowered with Vision-Based Hybrid Approach |
| title_full | Deep Transfer Learning-Based Animal Face Identification Model Empowered with Vision-Based Hybrid Approach |
| title_fullStr | Deep Transfer Learning-Based Animal Face Identification Model Empowered with Vision-Based Hybrid Approach |
| title_full_unstemmed | Deep Transfer Learning-Based Animal Face Identification Model Empowered with Vision-Based Hybrid Approach |
| title_short | Deep Transfer Learning-Based Animal Face Identification Model Empowered with Vision-Based Hybrid Approach |
| title_sort | deep transfer learning based animal face identification model empowered with vision based hybrid approach |
| topic | livestock identification livestock muzzle pattern identification horse identification automated horse identification yolo equine biometrics |
| url | https://www.mdpi.com/2076-3417/13/2/1178 |
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