Personal Identification Using Ears Based on Statistical Features

Alaa Tharwat Othman

摘要

Biometrics is an automated method of recognizing a person based on a physiological (e.g. face, iris, or retina) or behavioral (e.g. gait, signature, or dynamic keystrokes) characteristics. Ear recognition is one of the physiological biometrics' types that have been interested in the recent years. Ear recognition, achieves good accuracy and has many advantages such as it doesn't affected by expressions, health, and more stable than many other biometrics. However, it has many challenges such as the pose of the face, lighting variation, occlusion with hair or clothes. ? In this research, four proposed models are used to identify people using ear images. The first model used single feature extraction method based on single classifier. While, the second model used single feature extraction method based on multi-classifiers. The third model used feature combination techniques (parallel or serial) based on single classifier. Finally, in the fourth model multi-features and multi-classifiers are used. In this research, there are four methods that are used to extract the features, namely, extit {Principal Component Analysis} ( PCA ), extit {Linear Discriminant Analysis} ( LDA ), extit {Independent Component Analysis} ( ICA ), and extit {Discrete Cousin Transform} ( DCT ). Neural networks, decision tree, and minimum distance classifiers are used to classify the unknown samples. ? The occlusion problem with hair or scarves is one of the big challenges of the ear recognition systems. In this research, segmentation technique is proposed to neglect the occluded part and solve the occlusion problem. The idea of the segmentation technique is based on dividing the ear images into different parts. The occluded part/s is neglected and the rest of the parts are used to identify people based on features fusion and classifiers fusion. The segmentation technique consists of two main types, namely, uniform or non-uniform segmentation techniques. In this research, the uniform segmentation technique is used for many experiments (horizontal, vertical, and grid). All the four proposed models are applied to all ear segments to investigate the power of each model and to achieve a high accuracy. ? In this research, ear database images is used. The ear dataset consists of 102 grayscale images (6 images for each of 17 subjects) in PGM format [1] . ? The proposed models are achieved good identification rates using ear images. In the first model, the best accuracy achieved using LDA and neural network classifier. The results of the first model ranged from 64.12 % to 100 %. In the second model, many classifiers are fused to increase the recognition rate. In this method, two methods are used, namely, Borda count and majority voting. The results of this model ranged from 94.12 % to 96.08 %. The third model, the features using two different methods, namely serial and parallel are combined. The results of this model prove that the serial combination is more powerful than parallel combination. Finally, in the fourth model, two features and two classifiers are fused to get one decision. The accuracy of this model is approximately the same of the third model, and it does not achieve good results because there is a diversity between different classifiers. Moreover, the proposed segmentation model achieved good results when some parts of the ear images are occluded.

机译：生物识别是一种基于生理（例如面部，虹膜或视网膜）或行为（例如步态，特征或动态击键）特征的人的自动识别方法。耳识别是近年来受到关注的一种生理生物识别技术。耳朵识别功能具有很高的准确性，并且具有许多优势，例如不受表情，健康影响，并且比许多其他生物识别技术更稳定。但是，它面临许多挑战，例如面部姿势，光线变化，头发或衣服的咬合。？在这项研究中，四个提出的模型被用来识别使用耳朵图像的人。第一个模型使用基于单一分类器的单一特征提取方法。而第二个模型则使用了基于多分类器的单特征提取方法。第三个模型使用基于单个分类器的特征组合技术（并行或串行）。最后，在第四个模型中，使用了多个功能和多个分类器。在这项研究中，有四种用于提取特征的方法，即 textit {主成分分析}（PCA）， textit {线性判别分析}（LDA）， textit {独立成分分析}（ICA），以及 textit {离散表兄弟变换}（DCT）。神经网络，决策树和最小距离分类器用于对未知样本进行分类。？头发或围巾的咬合问题是耳朵识别系统的一大挑战。在这项研究中，提出了分割技术来忽略遮挡部分并解决遮挡问题。分割技术的思想是基于将耳朵图像分为不同的部分。被遮挡的部分被忽略，其余部分用于基于特征融合和分类器融合来识别人。分割技术包括两种主要类型，即统一或非均匀分割技术。在这项研究中，均匀分割技术用于许多实验（水平，垂直和网格）。所提出的所有四个模型都应用于所有耳段，以研究每个模型的功效并实现高精度。？在这项研究中，使用了耳朵数据库图像。耳朵数据集包含102幅PGM格式的灰度图像（每17个对象6幅图像）[1]。？所提出的模型使用耳朵图像获得了良好的识别率。在第一个模型中，使用LDA和神经网络分类器可获得最佳精度。第一个模型的结果范围从64.12 ％到100 ％。在第二个模型中，融合了许多分类器以提高识别率。在此方法中，使用了两种方法，即Borda计数和多数表决。该模型的结果范围从94.12 ％到96.08 ％。第三种模式，将使用两种不同方法的功能进行组合，即串行和并行。该模型的结果证明，串行组合比并行组合更强大。最后，在第四个模型中，将两个特征和两个分类器融合在一起以获得一个决策。该模型的准确性与第三个模型大致相同，并且由于不同分类器之间存在差异，因此无法获得良好的结果。此外，当遮挡耳朵图像的某些部分时，提出的分割模型取得了良好的效果。

Personal Identification Using Ears Based on Statistical Features

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