Esraa Alqaralleh

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Research Title :Ear Recognition Based on Fusion of Ear and Tragus Under Different Challenges

Authors :Esraa Alqaralleh and Önsen Toygar

Abstract :This paper proposes a 2D ear recognition approach that is based on the fusion of ear and tragus using score-level fusion strategy. An attempt to overcome the effect of partial occlusion, pose variation and weak illumination challenges is done since the accuracy of ear recognition may be reduced if one or more of these challenges are available. In this study, the effect of the aforementioned challenges is estimated separately, and many samples of ear that are affected by two different challenges concurrently are also considered. The tragus is used as a biometric trait because it is often free from occlusion; it also provides discriminative features even in different poses and illuminations. The features are extracted using local binary patterns and the evaluation has been done on three datasets of USTB database. It has been observed that the fusion of ear and tragus can improve the recognition performance compared to the unimodal systems. Experimental results show that the proposed method enhances the recognition rates by fusion of parts that are nonoccluded with tragus in the cases of partial occlusion, pose variation and weak illumination. It is observed that the proposed method performs better than feature-level fusion methods and most of the state-of-the-art ear recognition systems.

Keywords : Biometrics, ear recognition, tragus recognition, score-level fusion, ear occlusion, pose variations, illumination variations

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Research Title :Symmetric ear and profile face fusion for identical twins and non-twins recognition

Authors :Önsen Toygar, Esraa Alqaralleh & Ayman Afaneh

Abstract :Humans have bilateral body symmetry such that the left and right sides are mirror images of each other. This study tries to measure the performance on human recognition where the stored templates in the database are acquired from one side of a biometric trait such as left profile face, while the tested samples correspond to the other side of the same trait after applying a horizontal flip. Two different biometric traits are used in this study, namely profile face and ear biometrics. The experiments are conducted using the feature extraction methods namely Principal Component Analysis, Scale-Invariant Feature Transform, Local Binary Patterns, Local Phase Quantization and Binarized Statistical Image Features. Several experiments are performed on identical twins and non-twins individuals using ND-Twins-2009-2010 and UBEAR databases. Furthermore, the symmetry of profile face and ear is used to propose a hybrid approach of human recognition system that involves feature-level and score-level fusion of both traits. The proposed method is superior to all the unimodal and multimodal biometric methods that are implemented in this study for human recognition in the case of symmetry

Keywords :Biometrics, face recognition, score-level fusion, pose variations, illumination variations

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Research Title :On the use of ear and profile faces for distinguishing identical twins and nontwins

Authors :Önsen Toygar, Esraa Alqaralleh & Ayman Afaneh

Abstract :This study aims to measure the efficiency of ear and profile face in distinguishing identical twins under identification and verification modes. In addition, to distinguish identical twins by ear and profile face separately, we propose to fuse these traits with all possible binary combinations of left ear, left profile face, right ear, and right profile face. Fusion is implemented by score-level fusion and decision-level fusion techniques in the proposed method. Additionally, feature-level fusion is used for comparison. All experiments in this paper are also implemented on nontwins individuals, and the recognition performance of twins and nontwins are compared. Local binary patterns, local phase quantization, and binarized statistical image features approaches are used as texture-based descriptors for feature extraction process. Images under controlled and uncontrolled lighting are tested. Ear and profile images from ND-TWINS-2009-2010 dataset are used in the experiments. The experimental results show that the proposed method is more accurate and reliable than using ear or profile face images separately. The performance of the proposed method for recognizing identical twins as recognition rate is 100% and 99.45%, and equal error rates are 0.54% and 1.63% in controlled and uncontrolled illumination conditions, respectively.

Keywords :ear recognition, face recognition, identical twins, information fusion, texture descriptors

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Research Title : Masked face recognition using frontal and profile faces with multiple fusion levels

Authors :Esraa Alqaralleh , Ayman Afaneh and Önsen Toygar

Abstract :The main goal of this work is to construct a face recognition system that can overcome the limitation of masked face by exploiting the unmasked parts of frontal and profile faces. This study mainly depends on the upper part of frontal face that contains the eyes and forehead beside of the unmasked parts of profile face such as ear. Many experiments are conducted to measure the efficiency and reliability of face recognition system in case of pandemic where the people have to cover most of their face by mask which makes the process of face recognition harder. Binarized statistical image features approaches is used as texture-based descriptors for feature extraction process beside of convolutional neural networks. Additionally, four fusion approaches are implemented, namely sensor-level, feature-level, score-level and decision-level fusion. Images of frontal and profile face under different conditions from ND-TWINS-2009-2010 and CFPW datasets are used in the experiments. The experimental results show that the proposed method is more reliable and accurate in the case of masked face than using profile or frontal face images as a standalone system. The maximum performance of the proposed method for recognizing masked face as recognition rate is 99.83%. Moreover, the proposed system is better than the state-of-the-art CNN-based and texture-based systems using both frontal and profile faces with and without mask on the faces.

Keywords : Masked face ・ Pandemic ・ Information fusion ・ Biometrics ・ Face recognition

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