Real time ear recognition using deep learning

Alkababji, Ahmed M.; Mohammed, Omar H.

doi:10.12928/telkomnika.v19i2.18322

Cited by 23 publications

(14 citation statements)

References 28 publications

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“…The faster R-CNN method uses the region proposal network (RPN) to increase speed when perform objects recognition [31]- [34]. The RPN will receive input in the form of a feature map that has been processed by convolution.…”

Section: Transfer Learning With Faster R-cnn Methodsmentioning

confidence: 99%

Comparison of two deep learning methods for detecting fire hotspots

Lestari

Kosasih

2022

IJECE

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Every high-rise building must meet construction requirements, i.e. it must have good safety to prevent unexpected events such as fire incident. To avoid the occurrence of a bigger fire, surveillance using closed circuit television (CCTV) videos is necessary. However, it is impossible for security forces to monitor for a full day. One of the methods that can be used to help security forces is deep learning method. In this study, we use two deep learning methods to detect fire hotspots, i.e. you only look once (YOLO) method and faster region-based convolutional neural network (faster R-CNN) method. The first stage, we collected 100 image data (70 training data and 30 test data). The next stage is model training which aims to make the model can recognize fire. Later, we calculate precision, recall, accuracy, and F1 score to measure performance of model. If the F1 score is close to 1, then the balance is optimal. In our experiment results, we found that YOLO has a precision is 100%, recall is 54.54%, accuracy is 66.67%, and F1 score is 0.70583667. While faster R-CNN has a precision is 87.5%, recall is 95.45%, accuracy is 86.67%, and F1 score is 0.913022.

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Section: Transfer Learning With Faster R-cnn Methodsmentioning

confidence: 99%

Comparison of two deep learning methods for detecting fire hotspots

Lestari

Kosasih

2022

IJECE

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“…Alkababji and Mohammed [ 10 ] presented the use of a deep learning item detector called faster region-based convolutional neural network (Faster R-CNN) for ear detection. This CNN is used for feature extraction.…”

Section: Related Workmentioning

confidence: 99%

Exploration of Ear Biometrics Using EfficientNet

Booysens

Viriri

2022

Computational Intelligence and Neuroscience

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Biometrics is the recognition of a human using biometric characteristics for identification, which may be physiological or behavioral. The physiological biometric features are the face, ear, iris, fingerprint, and handprint; behavioral biometrics are signatures, voice, gait pattern, and keystrokes. Numerous systems have been developed to distinguish biometric traits used in multiple applications, such as forensic investigations and security systems. With the current worldwide pandemic, facial identification has failed due to users wearing masks; however, the human ear has proven more suitable as it is visible. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet. This paper presents the performance achieved in this research and shows the efficiency of EfficientNet on ear recognition. The nine variants of EfficientNets were fine-tuned and implemented on multiple publicly available ear datasets. The experiments showed that EfficientNet variant B8 achieved the best accuracy of 98.45%.

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“…This proposal gets classification accuracy up to 98.79%, 98.70%, and 84.30% for AWE, AMI, and WPUT ear datasets, respectively. Finally, the author in [36] evaluated a complete pipeline for ear recognition using a Faster Region-based CNN (Faster R-CNN) as object detector, a CNN as feature extractor, principal component analysis (PCA) for feature dimension reduction, a genetic algorithm for feature selection, and a fully connected artificial neural network for feature matching. Experimental results show the time needed for the complete pipeline execution, 76 ms for matching (database of 33 ear images features), 15 ms for feature extraction, and ear detection and localization requiring 100 ms. the total time to run the system is 191 ms, which can be used in real-time applications with high accuracy.…”

Section: Related Workmentioning

confidence: 99%

VGGFace-Ear: An Extended Dataset for Unconstrained Ear Recognition

Ramos-Cooper

Gomez-Nieto

Cámara-Chávez

2022

Sensors

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Recognition using ear images has been an active field of research in recent years. Besides faces and fingerprints, ears have a unique structure to identify people and can be captured from a distance, contactless, and without the subject’s cooperation. Therefore, it represents an appealing choice for building surveillance, forensic, and security applications. However, many techniques used in those applications—e.g., convolutional neural networks (CNN)—usually demand large-scale datasets for training. This research work introduces a new dataset of ear images taken under uncontrolled conditions that present high inter-class and intra-class variability. We built this dataset using an existing face dataset called the VGGFace, which gathers more than 3.3 million images. in addition, we perform ear recognition using transfer learning with CNN pretrained on image and face recognition. Finally, we performed two experiments on two unconstrained datasets and reported our results using Rank-based metrics.

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Real time ear recognition using deep learning

Cited by 23 publications

References 28 publications

Comparison of two deep learning methods for detecting fire hotspots

Comparison of two deep learning methods for detecting fire hotspots

Exploration of Ear Biometrics Using EfficientNet

VGGFace-Ear: An Extended Dataset for Unconstrained Ear Recognition

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