Ear-based biometric authentication through the detection of prominent contours

Kohlakala, Aviwe; Coetzer, Johannes

doi:10.23919/saiee.2021.9432897

Cited by 13 publications

(6 citation statements)

References 15 publications

(19 reference statements)

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“…Although the proposed methodology is specified to do ear detection, it could be extended to detect other parts of the face, given the right set of datasets. [4] 69.33 Raveane et al [5] 98 Zhang and Mu [6] 99.11 Kohlakala and Coetzer [7] 95.63 Tomczyk and Szczepaniak [8] NA Alshazly et al [9] 22 Alkababji and Mohammed [10] 97.8 Jamil et al [11] 97 Hansley et al [12] NA Average of our work 97.07…”

Section: Discussionmentioning

confidence: 85%

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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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Section: Discussionmentioning

confidence: 85%

“…Kohlakala and Coetzer [ 7 ] presented semi-automated and fully automated ear-based biometric verification systems. CNN and morphological postprocessing manually identify the ear region.…”

Section: Related Workmentioning

confidence: 99%

Exploration of Ear Biometrics Using EfficientNet

Booysens

Viriri

2022

Computational Intelligence and Neuroscience

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“…Kohlakala and Coetzer [66] presented semiautomated and fully automated ear-based biometric verification systems. A convolutional neural network (CNN) and Geometric deep learning (GDL) generalises convolutional neural network (CNN) to non-Euclidean domains, presented by [67] Tomczyk and Szczepaniak.…”

Section: Review Of Ear Algorithms Using Cnnmentioning

confidence: 99%

Ear Biometrics Using Deep Learning: A Survey

Booysens

Viriri

2022

Applied Computational Intelligence and Soft Computing

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This paper explores ear biometrics using a mixture of feature extraction techniques and classifies this feature vector using deep learning with convolutional neural network. This exploration of ear biometrics uses images from 2D facial profiles and facial images. The investigated feature techniques are Zernike Moments, local binary pattern, Gabor filter, and Haralick texture moments. The normalised feature vector is used to examine whether deep learning using convolutional neural network is better at identifying the ear than other commonly used machine learning techniques. The widely used machine learning techniques that were used to compare them are decision tree, naïve Bayes, K-nearest neighbors (KNN), and support vector machine (SVM). This paper proved that using a bag of feature techniques and the classification technique of deep learning using convolutional neural network was better than standard machine learning techniques. The result achieved by the deep learning using convolutional neural network was 92.00% average ear identification rate for both left and right ears.

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“…In addition, a person's ear outer structure stays the same over time or varies very little. To detect ear biometrics, several techniques can be used, such as feature-based template matching [1], [2], [3] and mathematical morphological operations [4], [5] including dilation, erosion, etc. Cropping ear images can benefit from computer vision because required details like shape, contour, edge, curves, and graphs are extracted through image processing; however, this process still necessitates a lot of manual image editing.…”

Section: Introductionmentioning

confidence: 99%

Comparison of YOLO (V3,V5) and MobileNet-SSD (V1,V2) for Person Identification Using Ear-Biometrics

Hossain,

Anzum,

Akhter

2024

IJCDS

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The ear is a visible organ with a unique structure for each person. As a result, it can be used as a biometric to circumvent the constraints of person identification. Deep learning methods like You Only Look Once (YOLO) and MobileNet have recently significantly aided real-time biometric recognition. As a result, in this paper, we approach identifying a person using YOLOV3, YOLOV5, MobileNet-SSDV1, and MobileNet-SSDV2 deep learning algorithms using their ear biometrics. The used ear biometric is a standard dataset (EarVN1.0 Dataset) from 164 individuals with a total of 27,592 images. We chose 10 people at random, totaling 2057 pictures. Of these, 85% were used for training, 5% for validation, and 10% for testing. The performance of the algorithms is determined based on their accuracy and how smoothly the ear of a person is detected. The training accuracy of the algorithms is thresholded at 99.87%. MobileNet-SSDV1, MobileNet-SSDV2, YOLOV3, and YOLOV5 have testing accuracy that is 88%, 91%, 95%, and 96%, respectively. We concluded that the YOLOV5 model outperforms the others in terms of accuracy and size (16MB) for person identification using ear biometrics.

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Ear-based biometric authentication through the detection of prominent contours

Cited by 13 publications

References 15 publications

Exploration of Ear Biometrics Using EfficientNet

Exploration of Ear Biometrics Using EfficientNet

Ear Biometrics Using Deep Learning: A Survey

Comparison of YOLO (V3,V5) and MobileNet-SSD (V1,V2) for Person Identification Using Ear-Biometrics

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