Can Convolution Neural Network (CNN) Triumph in Ear Recognition of Uniform Illumination Invariant?

Jamil, Nursuriati; Almisreb, Ali Abd; Ariffin, Syed Mohd Zahid Syed Zainal; Din, Norashidah Md; Hamzah, Raseeda

doi:10.11591/ijeecs.v11.i2.pp558-566

Cited by 9 publications

(8 citation statements)

References 19 publications

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“…Jamil et al [ 11 ] build and train a CNN model for ear biometrics in various uniform illuminations measured using lumens. They considered that their work was the first to test the performance of CNN on underexposed or overexposed images.…”

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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Section: Related Workmentioning

confidence: 99%

Exploration of Ear Biometrics Using EfficientNet

Booysens

Viriri

2022

Computational Intelligence and Neuroscience

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“…Jamil et al [70] built and trained a CNN model for ear biometrics in various uniform illuminations measured using lumens. ey considered that their work was the first to test the performance of CNN on very underexposed or overexposed images.…”

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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“…Although there are several methods available for ear recognition, here, we focus mainly on CNN‐based ear detection methods. Zhang and Mu have proposed a method based on deep learning approach to segment ear from the images.…”

Section: Related Workmentioning

confidence: 99%

Unconstrained ear detection using ensemble‐based convolutional neural network model

Ganapathi

Prakash

Dave

et al. 2019

Concurrency and Computation

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This paper presents a technique for ear detection from 2D profile face images that is capable of significantly reducing the false positives. In an ear biometrics system, recognition performance highly depends on the performance of the ear detection module. The trade-off between the complexity and the false positive detection is one of the essential component, where the complexity of a system increases proportionally to achieve a zero false positive rate detection.In literature, available ear detection techniques based on handcrafted features face challenges with low-quality acquired images affected by illumination, occlusion, and pose variations. We propose an ear detection technique using ensemble of convolutional neural network (CNN). The first part of the technique trains three models of CNN on a given dataset, whereas in later part, weighted average of the outputs of trained models is utilized to detect the ear regions. The used ensemble models show better performance as compared to the case when each individual model is used standalone. The proposed technique is being evaluated on two databases, viz, IIT Indore-Collection A (IIT-Col A) database and annotated web ear (AWE) database. Experimental results of ear detection demonstrate the superior performance of the proposed technique over other state-of-the-art techniques in handling illumination, occlusion, and pose variations. KEYWORDS deep learning, ear detection, ensemble model, unconstrained environment INTRODUCTIONIn recent years, ear biometrics has been considered a reliable biometrics and has got attention over face mainly due to its invariant behavior to the age of a person. Biometrics systems based on face have to consider the changes occurring in the face with a change in age or expression.The ear of a person remains consistent in its shape and it is fixed in its position at the center of the profile image. To obtain reliable performance, face images are required to be captured in the controlled setting, while in case of ear, it can be captured with a foreseeable background due to its fixed side face background. An ear biometric system mainly consists of two processes, ie, Ear detection and Ear recognition. Majority of the ear biometrics techniques in the literature use manually cropped ear images to perform ear recognition process. Furthermore, the ear biometric techniques that crops ear automatically are able to detect ear only when the ear has small variations in the side face image. In cases of pose or scale variations, these techniques are found to be inefficient. Additionally, in a real-time environment, these techniques lack in being fully automatic approaches and speed.Performance of an ear detection technique based on handcrafted feature learning methods depends on the dataset used for learning the features. 1 These methods are prone to produce erroneous results when used with the images captured in unconstrained settings like different illumination, background, and pose. Tasks like extraction of suitable features along with the discriminati...

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Can Convolution Neural Network (CNN) Triumph in Ear Recognition of Uniform Illumination Invariant?

Cited by 9 publications

References 19 publications

Exploration of Ear Biometrics Using EfficientNet

Exploration of Ear Biometrics Using EfficientNet

Ear Biometrics Using Deep Learning: A Survey

Unconstrained ear detection using ensemble‐based convolutional neural network model

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