Potency of Individual Identification of Japanese Macaques (Macaca fuscata) Using a Face Recognition System and a Limited Number of Learning Images

Mammal Study ◽  
2021 ◽  
Vol 46 (1) ◽  
Author(s):  
Yosuke Otani ◽  
Hitoshi Ogawa
2020 ◽  
Author(s):  
Yosuke Otani ◽  
Hitoshi Ogawa

AbstractIndividual identification is an important technique in animal research that requires researcher training and specialized skillsets. Face recognition systems using artificial intelligence (AI) deep learning have been put into practical use to identify in humans and animals, but a large number of annotated learning images are required for system construction. In wildlife research cases, it is difficult to prepare a large amount of learning images, which may be why systems using AI have not been widely used in field research. To investigate the development of a system that identifies individuals using a small number of learning images, we constructed a system to identify individual Japanese macaques (Macaca fuscata yakui) with a low error rate from an average of 20 images per individual. The characteristics of this system were augmentation of data, simultaneous determination by four individual identification models and identification from a majority of five frames to ensure reliability. This technology has a high degree of utility for various stakeholders and it is expected that it will advance the development of individual identification systems by AI that can be widely used in field research.


BMC Zoology ◽  
2017 ◽  
Vol 2 (1) ◽  
Author(s):  
David Crouse ◽  
Rachel L. Jacobs ◽  
Zach Richardson ◽  
Scott Klum ◽  
Anil Jain ◽  
...  

2020 ◽  
Vol 1601 ◽  
pp. 052011
Author(s):  
Yong Li ◽  
Zhe Wang ◽  
Yang Li ◽  
Xu Zhao ◽  
Hanwen Huang

Author(s):  
CHING-WEN CHEN ◽  
CHUNG-LIN HUANG

This paper presents a face recognition system which can identify the unknown identity effectively using the front-view facial features. In front-view facial feature extractions, we can capture the contours of eyes and mouth by the deformable template model because of their analytically describable shapes. However, the shapes of eyebrows, nostrils and face are difficult to model using a deformable template. We extract them by using the active contour model (snake). After the contours of all facial features have been captured, we calculate effective feature values from these extracted contours and construct databases for unknown identities classification. In the database generation phase, 12 models are photographed, and feature vectors are calculated for each portrait. In the identification phase if any one of these 12 persons has his picture taken again, the system can recognize his identity.


Sensors ◽  
2014 ◽  
Vol 14 (11) ◽  
pp. 21726-21749 ◽  
Author(s):  
Won Lee ◽  
Yeong Kim ◽  
Hyung Hong ◽  
Kang Park

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