Iris photo-identification: A new methodology for the individual recognition of Tarentola geckos

2013 ◽  
Vol 34 (4) ◽  
pp. 590-596 ◽  
Author(s):  
Ricardo Rocha ◽  
Tiago Carrilho ◽  
Rui Rebelo
Keyword(s):  
Pattern Matching ◽  
Individual Recognition ◽  
Field Studies ◽  
Individual Identification ◽  
Identification System ◽  
Correct Identification ◽  
Photo Identification ◽  
Horizontal Angle ◽  
The Individual

Gekkonid field studies are hampered by the difficulty to individually recognize individuals. In this study we assess the feasibility of using their variegated iris pattern to photo-identify Tarentola boettgeri bischoffi, a threatened Macaronesian endemic. Using a library of 924 photos taken over a 9-month period we also evaluate the use of the pattern matching software Interactive Individual Identification System (I3S) to match photos of known specimens. Individuals were clearly recognized by their iris pattern with no misidentifications, and using I3S lead to a correct identification of 95% of the recaptures in a shorter time than the same process when conducted visually by an observer. The method’s feasibility was improved by increasing the number of images of each animal in the library and hindered by photos that deviate from a horizontal angle.

scholarly journals Research on emitter individual identification technology based on Automatic Dependent Surveillance–Broadcast signal

2021 ◽  
Vol 17 (2) ◽  
pp. 155014772199262
Author(s):  
Shiwen Chen ◽  
Junjian Yuan ◽  
Xiaopeng Xing ◽  
Xin Qin
Keyword(s):  
Individual Recognition ◽  
Evidence Theory ◽  
Field Measurements ◽  
Training Data ◽  
Individual Identification ◽  
Identification System ◽  
Individual Feature ◽  
Data Set ◽  
Theoretical Simulation ◽  
The Individual

Aiming at the shortcomings of the research on individual identification technology of emitters, which is primarily based on theoretical simulation and lack of verification equipment to conduct external field measurements, an emitter individual identification system based on Automatic Dependent Surveillance–Broadcast is designed. On one hand, the system completes the individual feature extraction of the signal preamble. On the other hand, it realizes decoding of the transmitter’s individual identity information and generates an individual recognition training data set, on which we can train the recognition network to achieve individual signal recognition. For the collected signals, six parameters were extracted as individual features. To reduce the feature dimensions, a Bessel curve fitting method is used for four of the features. The spatial distribution of the Bezier curve control points after fitting is taken as an individual feature. The processed features are classified with multiple classifiers, and the classification results are fused using the improved Dempster–Shafer evidence theory. Field measurements show that the average individual recognition accuracy of the system reaches 88.3%, which essentially meets the requirements.

scholarly journals I3S Classic and Insect Species Identification of Diptera and Hymenoptera (Mosquitoes and Bees)

2016 ◽  
Author(s):  
Nayna Vyas-Patel ◽  
John D Mumford
Keyword(s):  
Apis Mellifera ◽  
Image Recognition ◽  
Bombus Terrestris ◽  
Individual Recognition ◽  
Sensu Stricto ◽  
Individual Identification ◽  
Insect Species ◽  
Identification System ◽  
Accurate Identification ◽  
The Individual

AbstractA number of image recognition systems have been specifically formulated for the individual recognition of large animals. These programs are versatile and can easily be adapted for the identification of smaller individuals such as insects. The Interactive Individual Identification System, I3S Classic, initially produced for the identification of individual whale sharks was employed to distinguish between different species of mosquitoes and bees, utilising the distinctive vein pattern present on insect wings. I3S Classic proved to be highly effective and accurate in identifying different species and sexes of mosquitoes and bees, with 80% to100% accuracy for the majority of the species tested. The sibling species Apis mellifera and Apis mellifera carnica were both identified with100% accuracy. Bombus terrestris terrestris and Bombus terrestris audax; were also identified and separated with high degrees of accuracy (90% to 100% respectively for the fore wings and 100% for the hind wings). When both Anopheles gambiae sensu stricto and Anopheles arabiensis were present in the database, they were identified with 94% and 100% accuracy respectively, allowing for a morphological and non-molecular method of sorting between these members of the sibling complex. Flat, not folded and entire, rather than broken, wing specimens were required for accurate identification. Only one wing image of each sex was required in the database to retrieve high levels of accurate results in the majority of species tested. The study describes how I3S was used to identify different insect species and draws comparisons with the use of the CO1 algorithm. As with CO1, I3S Classic proved to be suitable software which could reliably be used to aid the accurate identification of insect species. It is emphasised that image recognition for insect species should always be used in conjunction with other identifying characters in addition to the wings, as is the norm when identifying species using traditional taxonomic keys.

Individual Identification Using Images of Eyes

1997 ◽  
Vol 9 (1) ◽  
pp. 41-45
Author(s):  
Satoshi Tanigawa ◽  
◽  
Masafumi Uchida ◽  
Hideto Ide
Keyword(s):  
Individual Identification ◽  
Identification System ◽  
Information Industry ◽  
Business Information ◽  
Crime Investigation ◽  
Security Business ◽  
The Individual ◽  
Business Security

Individual identification is required in various fields such as credit business, security business, information industry and crime investigation. This paper describes the individual identification system using images of eyes. With this system having used images data of 20 registered persons and 20 unregistered persons, we could obtain a high recogniniton rate and showing how efficient this system is.

scholarly journals A simple tool for linking photo-identification with multimedia data to track mammal behaviour

2021 ◽  
Author(s):  
Alexandre M. S. Machado ◽  
Mauricio Cantor
Keyword(s):  
Bottlenose Dolphins ◽  
R Package ◽  
Multimedia Data ◽  
Raw Material ◽  
Individual Identification ◽  
Photo Identification ◽  
In The Wild ◽  
The Individual ◽  
Future Work ◽  
Free Ranging

AbstractIdentifying individual animals is critical to describe demographic and behavioural patterns, and to investigate the ecological and evolutionary underpinnings of these patterns. The traditional non-invasive method of individual identification in mammals—comparison of photographed natural marks—has been improved by coupling other sampling methods, such as recording overhead video, audio and other multimedia data. However, aligning, linking and syncing these multimedia data streams are persistent challenges. Here, we provide computational tools to streamline the integration of multiple techniques to identify individual free-ranging mammals when tracking their behaviour in the wild. We developed an open-source R package for organizing multimedia data and for simplifying their processing a posteriori—“MAMMals: Managing Animal MultiMedia: Align, Link, Sync”. The package contains functions to (i) align and link the individual data from photographs to videos, audio recordings and other text data sources (e.g. GPS locations) from which metadata can be accessed; and (ii) synchronize and extract the useful multimedia (e.g. videos with audios) containing photo-identified individuals. To illustrate how these tools can facilitate linking photo-identification and video behavioural sampling in situ, we simultaneously collected photos and videos of bottlenose dolphins using off-the-shelf cameras and drones, then merged these data to track the foraging behaviour of individuals and groups. We hope our simple tools encourage future work that extend and generalize the links between multiple sampling platforms of free-ranging mammals, thereby improving the raw material needed for generating new insights in mammalian population and behavioural ecology.

Photo Identification of Sea Turtles Using AlexNet and Multi-Class SVM

2020 ◽  
Author(s):  
Wan Nural Jawahir Hj Wan Yussof ◽  
Nurfarahim Shaharudin ◽  
Muhammad Suzuri Hitam ◽  
Ezmahamrul Afreen Awalludin ◽  
Mohd Uzair Rusli ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sea Turtles ◽  
Sea Turtle ◽  
Identification System ◽  
Turtle Species ◽  
Photo Identification ◽  
Biodiversity Research ◽  
The Individual ◽  
Error Correcting Output Codes

Up to now, identification of sea turtle species mainly for tracking the population usually relied on flipper tags or through other physical markers. However, this approach is not practical due to the missing tags over some period. Due to this matter, we propose a photo identification system of the individual sea turtle based on the convolutional neural network (CNN) using a pre-trained AlexNet CNN and error-correcting output codes (ECOC) SVM. Experiments were performed on 300 images obtained from Biodiversity Research Center, Academia Sinica, Taiwan. Using Alexnet and ECOC SVM, the overall accuracy achieved is 62.9%. The results indicate that features obtained from the CNN are capable of identifying photo of sea turtles.

scholarly journals FIN-PRINT a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whales

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Christian Bergler ◽  
Alexander Gebhard ◽  
Jared R. Towers ◽  
Leonid Butyrev ◽  
Gary J. Sutton ◽  
...  
Keyword(s):  
Deep Learning ◽  
North America ◽  
Killer Whale ◽  
Individual Recognition ◽  
Killer Whales ◽  
Photo Identification ◽  
Learning Framework ◽  
Multi Stage ◽  
The Individual ◽  
Identification Techniques

AbstractBiometric identification techniques such as photo-identification require an array of unique natural markings to identify individuals. From 1975 to present, Bigg’s killer whales have been photo-identified along the west coast of North America, resulting in one of the largest and longest-running cetacean photo-identification datasets. However, data maintenance and analysis are extremely time and resource consuming. This study transfers the procedure of killer whale image identification into a fully automated, multi-stage, deep learning framework, entitled FIN-PRINT. It is composed of multiple sequentially ordered sub-components. FIN-PRINT is trained and evaluated on a dataset collected over an 8-year period (2011–2018) in the coastal waters off western North America, including 121,000 human-annotated identification images of Bigg’s killer whales. At first, object detection is performed to identify unique killer whale markings, resulting in 94.4% recall, 94.1% precision, and 93.4% mean-average-precision (mAP). Second, all previously identified natural killer whale markings are extracted. The third step introduces a data enhancement mechanism by filtering between valid and invalid markings from previous processing levels, achieving 92.8% recall, 97.5%, precision, and 95.2% accuracy. The fourth and final step involves multi-class individual recognition. When evaluated on the network test set, it achieved an accuracy of 92.5% with 97.2% top-3 unweighted accuracy (TUA) for the 100 most commonly photo-identified killer whales. Additionally, the method achieved an accuracy of 84.5% and a TUA of 92.9% when applied to the entire 2018 image collection of the 100 most common killer whales. The source code of FIN-PRINT can be adapted to other species and will be publicly available.

scholarly journals Shot the spots: A reliable field method for individual identification of Amolops formosus (Anura, Ranidae)

Herpetozoa ◽  
2020 ◽  
Vol 33 ◽  
pp. 7-15
Author(s):  
Naitik G. Patel ◽  
Abhijit Das
Keyword(s):  
Image Quality ◽  
Individual Recognition ◽  
Field Method ◽  
Individual Identification ◽  
Abundance Estimate ◽  
Recognition Efficiency ◽  
The Difference ◽  
Body Patterns ◽  
The Individual ◽  
User Review

Natural body patterns in amphibians are widely used for individual recognition. In this study, we photographed individuals of Amolops formosus for four days of sampling without handling them. We processed 301 photographs of dorsal blotch pattern in HotSpotter software and verified them visually for confirmation. We identified 160 unique individuals of A. formosus based on the images taken in the field, resulting in an abundance estimate of 180 individuals. The success rate in identifying individuals of A. formosus using the HotSpotter software was 94.3%. We tested the effect of image quality and distance on recognition efficiency. Poor image quality reduced the recognition efficiency of the software but with a careful user review it was possible to identify the individual. The difference between using only the software and software plus human confirmation was very small. This protocol is useful for rapid population estimation of frogs with natural body patterns.

scholarly journals A Novel Two-Level Fusion Feature for Mixed ECG Identity Recognition

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2052
Author(s):  
Xin Liu ◽  
Yujuan Si ◽  
Weiyi Yang
Keyword(s):  
Principal Component ◽  
Individual Identification ◽  
Support Vector ◽  
Identification System ◽  
Identity Recognition ◽  
Deep Feature ◽  
Single Feature ◽  
The Individual ◽  
Fusion Feature ◽  
Level Fusion

In recent years, with the increasing standard of biometric identification, it is difficult to meet the requirements of data size and accuracy in practical application for training a single ECG (electrocardiogram) database. The paper aims to construct a recognition model for processing multi-source data and proposes a novel ECG identification system based on two-level fusion features. Firstly, the features of Hilbert transform and power spectrum are extracted from the segmented heartbeat data, then two features are combined into a set and normalized to obtain the elementary fusion feature. Secondly, PCANet (Principal Component Analysis Network) is used to extract the discriminative deep feature of signal, and MF (MaxFusion) algorithm is proposed to fuse and compress the two layers learning features. Finally, a linear support vector machine (SVM) is used to obtain labels of single feature classification and complete the individual identification. The recognition results of the proposed two-level fusion PCANet deep recognition network achieve more than 95% on ECG-ID, MIT-BIH, and PTB public databases. Most importantly, the recognition accuracy of the mixed database can reach 99.77%, which includes 426 individuals.

Double Line Clustering based Colour Image Segmentation Technique for Plant Disease Detection

2019 ◽  
Vol 15 (8) ◽  
pp. 769-776
Author(s):  
Kalaivani Subramani ◽  
Shantharajah Periyasamy ◽  
Padma Theagarajan
Keyword(s):  
Pattern Matching ◽  
Error Rate ◽  
Median Filter ◽  
Identification System ◽  
Double Line ◽  
Cultivation System ◽  
Matching Algorithm ◽  
Non Local ◽  
Mining Methods ◽  
Blight Disease

Background: Agriculture is one of the most essential industry that fullfills people’s need and also plays an important role in economic evolution of the nation. However, there is a gap between the agriculture sector and the technological industry and the agriculture plants are mostly affected by diseases, such as the bacterial, fungus and viral diseases that lead to loss in crop yield. The affected parts of the plants need to be identified at the beginning stage to eliminate the huge loss in productivity. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Conclusion: The result of the clustering algorithm achieved high accuracy, sensitivity, and specificity. The feature extraction is applied after the clustering process which produces minimum error rate.

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