Canine Biometric Identification Using ECG Signals and CNN-LSTM Neural Networks
As global pet acceptance increases, the market size for pet ownership grows. Consequently, registering pets is becoming increasingly crucial, with some nations mandating it by law. Animal biometrics is a subject of ongoing research, spanning inscriptions, iris recognition, and facial recognition, wi...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10365144/ |
| _version_ | 1797373138444484608 |
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| author | Min Keun Cho Tae Seon Kim |
| author_facet | Min Keun Cho Tae Seon Kim |
| author_sort | Min Keun Cho |
| collection | DOAJ |
| description | As global pet acceptance increases, the market size for pet ownership grows. Consequently, registering pets is becoming increasingly crucial, with some nations mandating it by law. Animal biometrics is a subject of ongoing research, spanning inscriptions, iris recognition, and facial recognition, with a growing number of companies partaking. However, biometric methods mostly rely on image recognition, which can result in degraded performance depending on the captured angle and external environment. To address this issue, we conducted a study to design and evaluate the performance of a deep learning-based dog identity recognition system that utilizes electrocardiogram (ECG) that is harder to forge than existing methods and does not require additional image processing. To evaluate performance, we utilized two dog ECG databases and conducted biometric recognition experiments with data collected from differing measurement environments from these integrated databases. Input signals for recognition were generated through both R-peak based and blind signal segmentation methods. For the purpose of dog identification, we developed and employed a 1D CNN-LSTM model as a classifier. Additionally, three DNN-based classifiers were developed to compare their performance with that of the proposed model. To evaluate performance, the confusion matrix was used in conjunction with metrics such as accuracy, equal error rate (EER), receiver operating characteristic (ROC) curve, and precision recall (PR) curve. The proposed model demonstrated up to 98.7% accuracy in the biometrics of a separate database of 16 subjects, and as high as 96.3% accuracy in the biometrics of an integrated dataset of 33 subjects. The suggested approach exhibited a 93.1% accuracy rate when employing the blind segmentation method, eliminating the need for supplementary signal processing to derive input signals. |
| first_indexed | 2024-03-08T18:46:01Z |
| format | Article |
| id | doaj.art-5fa93571353442058f2506881d37657d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T18:46:01Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5fa93571353442058f2506881d37657d2023-12-29T00:03:27ZengIEEEIEEE Access2169-35362023-01-011114573214574610.1109/ACCESS.2023.334445210365144Canine Biometric Identification Using ECG Signals and CNN-LSTM Neural NetworksMin Keun Cho0Tae Seon Kim1https://orcid.org/0000-0002-9250-186XSchool of Information, Communications, and Electronics Engineering, The Catholic University of Korea, Bucheon-si, Republic of KoreaSchool of Information, Communications, and Electronics Engineering, The Catholic University of Korea, Bucheon-si, Republic of KoreaAs global pet acceptance increases, the market size for pet ownership grows. Consequently, registering pets is becoming increasingly crucial, with some nations mandating it by law. Animal biometrics is a subject of ongoing research, spanning inscriptions, iris recognition, and facial recognition, with a growing number of companies partaking. However, biometric methods mostly rely on image recognition, which can result in degraded performance depending on the captured angle and external environment. To address this issue, we conducted a study to design and evaluate the performance of a deep learning-based dog identity recognition system that utilizes electrocardiogram (ECG) that is harder to forge than existing methods and does not require additional image processing. To evaluate performance, we utilized two dog ECG databases and conducted biometric recognition experiments with data collected from differing measurement environments from these integrated databases. Input signals for recognition were generated through both R-peak based and blind signal segmentation methods. For the purpose of dog identification, we developed and employed a 1D CNN-LSTM model as a classifier. Additionally, three DNN-based classifiers were developed to compare their performance with that of the proposed model. To evaluate performance, the confusion matrix was used in conjunction with metrics such as accuracy, equal error rate (EER), receiver operating characteristic (ROC) curve, and precision recall (PR) curve. The proposed model demonstrated up to 98.7% accuracy in the biometrics of a separate database of 16 subjects, and as high as 96.3% accuracy in the biometrics of an integrated dataset of 33 subjects. The suggested approach exhibited a 93.1% accuracy rate when employing the blind segmentation method, eliminating the need for supplementary signal processing to derive input signals.https://ieeexplore.ieee.org/document/10365144/Biometricscanine identificationdeep learningelectrocardiogram (ECG) signal |
| spellingShingle | Min Keun Cho Tae Seon Kim Canine Biometric Identification Using ECG Signals and CNN-LSTM Neural Networks IEEE Access Biometrics canine identification deep learning electrocardiogram (ECG) signal |
| title | Canine Biometric Identification Using ECG Signals and CNN-LSTM Neural Networks |
| title_full | Canine Biometric Identification Using ECG Signals and CNN-LSTM Neural Networks |
| title_fullStr | Canine Biometric Identification Using ECG Signals and CNN-LSTM Neural Networks |
| title_full_unstemmed | Canine Biometric Identification Using ECG Signals and CNN-LSTM Neural Networks |
| title_short | Canine Biometric Identification Using ECG Signals and CNN-LSTM Neural Networks |
| title_sort | canine biometric identification using ecg signals and cnn lstm neural networks |
| topic | Biometrics canine identification deep learning electrocardiogram (ECG) signal |
| url | https://ieeexplore.ieee.org/document/10365144/ |
| work_keys_str_mv | AT minkeuncho caninebiometricidentificationusingecgsignalsandcnnlstmneuralnetworks AT taeseonkim caninebiometricidentificationusingecgsignalsandcnnlstmneuralnetworks |