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http://dx.doi.org/10.5762/KAIS.2020.21.12.1

Recognition of dog's front face using deep learning and machine learning  

Kim, Jong-Bok (National Institute of Animal Science, Rural Development Administration)
Jang, Dong-Hwa (National Institute of Animal Science, Rural Development Administration)
Yang, Kayoung (National Institute of Animal Science, Rural Development Administration)
Kwon, Kyeong-Seok (National Institute of Animal Science, Rural Development Administration)
Kim, Jung-Kon (National Institute of Animal Science, Rural Development Administration)
Lee, Joon-Whoan (Division of Computer Science and Engineering, Jeonbuk National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.21, no.12, 2020 , pp. 1-9 More about this Journal
Abstract
As pet dogs rapidly increase in number, abandoned and lost dogs are also increasing in number. In Korea, animal registration has been in force since 2014, but the registration rate is not high owing to safety and effectiveness issues. Biometrics is attracting attention as an alternative. In order to increase the recognition rate from biometrics, it is necessary to collect biometric images in the same form as much as possible-from the face. This paper proposes a method to determine whether a dog is facing front or not in a real-time video. The proposed method detects the dog's eyes and nose using deep learning, and extracts five types of directional face information through the relative size and position of the detected face. Then, a machine learning classifier determines whether the dog is facing front or not. We used 2,000 dog images for learning, verification, and testing. YOLOv3 and YOLOv4 were used to detect the eyes and nose, and Multi-layer Perceptron (MLP), Random Forest (RF), and the Support Vector Machine (SVM) were used as classifiers. When YOLOv4 and the RF classifier were used with all five types of the proposed face orientation information, the face recognition rate was best, at 95.25%, and we found that real-time processing is possible.
Keywords
Dog; Front Face; Biometrics; YOLO; Machine Learning;
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