Dog Identification using Soft Biometrics and Neural Networks

Lai, Kenneth; Tu, Xinyuan; Yanushkevich, Svetlana

doi:10.1109/ijcnn.2019.8851971

Cited by 12 publications

(8 citation statements)

References 13 publications

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“…Kenneth et al [14] used deep neural networks to determine dog identity. They used both the photos and extra information about dogs, gender, and breed, to help with identification.…”

Section: Related Workmentioning

confidence: 99%

“…Our study employs body identification in addition to face and soft biometrics and utilizes different methodologies in framework architecture. We compared our findings with the most recent research on pet identification conducted by Kenneth et al [14], and the summary of this comparison can be found in Table 4. According to the table, our framework achieved an accuracy of 80% for the top similarity match for both cat and dog faces, whereas [17] only achieved 78.09% accuracy for dog faces.…”

Section: Identification and Recommendationsmentioning

confidence: 99%

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Deep Learning Pet Identification Using Face and Body

Azizi

Zaman

2023

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According to the American Humane Association, millions of cats and dogs are lost yearly. Only a few thousand of them are found and returned home. In this work, we use deep learning to help expedite the procedure of finding lost cats and dogs, for which a new dataset is collected. We applied transfer learning methods on different convolutional neural networks for species classification and animal identification. The framework consists of seven sequential layers: data preprocessing, species classification, face and body detection with landmark detection techniques, face alignment, identification, animal soft biometrics, and recommendation. We achieved an accuracy of 98.18% on species classification. In the face identification layer, 80% accuracy was achieved. Body identification resulted in 81% accuracy. When using body identification in addition to face identification, the accuracy increased to 86.5%, with a 100% chance that the animal would be in our top 10 recommendations of matching. By incorporating animals’ soft biometric information, the system can identify animals with 92% confidence.

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“…Kenneth et al [14] used deep neural networks to determine dog identity. They used both the photos and extra information about dogs, gender, and breed, to help with identification.…”

Section: Related Workmentioning

confidence: 99%

Section: Identification and Recommendationsmentioning

confidence: 99%

Deep Learning Pet Identification Using Face and Body

Azizi

Zaman

2023

Information

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“…Hu and You [10] used ResNet18 on the 19,465-image Animal-10 dataset to obtain an accuracy of 92%. Lai et al [11] trained Xception on the Stanford Dogs Dataset of 20,580 images and 120 breeds to achieve a species classification accuracy of 91.29%. Tabak et al [12] trained a ResNet-18 CNN on a 3.37 million-image dataset comprising of images from five U.S. states and achieved a multi-class classification accuracy of 97.6%.…”

Section: Animal Image Classification Using Convolutional Neural Networkmentioning

confidence: 99%

An IoT System Using Deep Learning to Classify Camera Trap Images on the Edge

Zualkernan

Dhou

Judas³

et al. 2022

Computers

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Camera traps deployed in remote locations provide an effective method for ecologists to monitor and study wildlife in a non-invasive way. However, current camera traps suffer from two problems. First, the images are manually classified and counted, which is expensive. Second, due to manual coding, the results are often stale by the time they get to the ecologists. Using the Internet of Things (IoT) combined with deep learning represents a good solution for both these problems, as the images can be classified automatically, and the results immediately made available to ecologists. This paper proposes an IoT architecture that uses deep learning on edge devices to convey animal classification results to a mobile app using the LoRaWAN low-power, wide-area network. The primary goal of the proposed approach is to reduce the cost of the wildlife monitoring process for ecologists, and to provide real-time animal sightings data from the camera traps in the field. Camera trap image data consisting of 66,400 images were used to train the InceptionV3, MobileNetV2, ResNet18, EfficientNetB1, DenseNet121, and Xception neural network models. While performance of the trained models was statistically different (Kruskal–Wallis: Accuracy H(5) = 22.34, p < 0.05; F1-score H(5) = 13.82, p = 0.0168), there was only a 3% difference in the F1-score between the worst (MobileNet V2) and the best model (Xception). Moreover, the models made similar errors (Adjusted Rand Index (ARI) > 0.88 and Adjusted Mutual Information (AMU) > 0.82). Subsequently, the best model, Xception (Accuracy = 96.1%; F1-score = 0.87; F1-Score = 0.97 with oversampling), was optimized and deployed on the Raspberry Pi, Google Coral, and Nvidia Jetson edge devices using both TenorFlow Lite and TensorRT frameworks. Optimizing the models to run on edge devices reduced the average macro F1-Score to 0.7, and adversely affected the minority classes, reducing their F1-score to as low as 0.18. Upon stress testing, by processing 1000 images consecutively, Jetson Nano, running a TensorRT model, outperformed others with a latency of 0.276 s/image (s.d. = 0.002) while consuming an average current of 1665.21 mA. Raspberry Pi consumed the least average current (838.99 mA) with a ten times worse latency of 2.83 s/image (s.d. = 0.036). Nano was the only reasonable option as an edge device because it could capture most animals whose maximum speeds were below 80 km/h, including goats, lions, ostriches, etc. While the proposed architecture is viable, unbalanced data remain a challenge and the results can potentially be improved by using object detection to reduce imbalances and by exploring semi-supervised learning.

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“…Previous studies have primarily focused on the face and body of the dog to distinguish the breeds [22][23][24][25][26]. However, there has been a lack of studies on dog behavior recognition and classification, which is the ultimate goal in the field of behavior recognition.…”

Section: Creation and Selection Of Behaviorsmentioning

confidence: 99%

An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors

2021

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Dogs and cats tend to show their conditions and desires through their behaviors. In companion animal behavior recognition, behavior data obtained by attaching a wearable device or sensor to a dog’s body are mostly used. However, differences occur in the output values of the sensor when the dog moves violently. A tightly coupled RGB time tensor network (TRT-Net) is proposed that minimizes the loss of spatiotemporal information by reflecting the three components (x-, y-, and z-axes) of the skeleton sequences in the corresponding three channels (red, green, and blue) for the behavioral classification of dogs. This paper introduces the YouTube-C7B dataset consisting of dog behaviors in various environments. Based on a method that visualizes the Conv-layer filters in analyzable feature maps, we add reliability to the results derived by the model. We can identify the joint parts, i.e., those represented as rows of input images showing behaviors, learned by the proposed model mainly for making decisions. Finally, the performance of the proposed method is compared to those of the LSTM, GRU, and RNN models. The experimental results demonstrate that the proposed TRT-Net method classifies dog behaviors more effectively, with improved accuracy and F1 scores of 7.9% and 7.3% over conventional models.

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Dog Identification using Soft Biometrics and Neural Networks

Cited by 12 publications

References 13 publications

Deep Learning Pet Identification Using Face and Body

Deep Learning Pet Identification Using Face and Body

An IoT System Using Deep Learning to Classify Camera Trap Images on the Edge

An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors

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