Person Recognition Through Profiler Faces Using Ear Biometrics

Recent studies in biometric systems have shown that the ear biometric is a reliable biometric for human recognition and among a lot of biometric traits it has achieved satisfying results for human recognition. In this thesis, 2D ear recognition approach based on the fusion of ear and tragus (small outer part of ear) using score-level fusion strategy is proposed. An attempt to overcome the effect of challenges such as partial occlusion, pose variation and weak illumination is done since the accuracy of ear recognition may be reduced if one or more of these challenges are available. In this thesis, the effect of the aforementioned challenges is estimated separately, and many samples of ear that are affected by two different challenges at the same time 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 (LBP) and the evaluation has been done on four datasets, namely USTB-1, USTB-2, USTB-3 and UBEAR. It has been observed that the fusion of ear and tragus can improve the recognition performance compared to the use of ear or tragus systems individually. Experimental results show that the proposed approach 1 enhances the recognition rates by fusion of parts that are non-occluded such as tragus in cases of partial occlusion, pose variation and weak illumination. It is observed that the proposed approach 1 that uses score-level fusion strategy performs better than feature-level fusion methods. Additionally, the proposed approach 1 performs better than most of the state-of-the-art ear recognition systems. Experimental results on three datasets show that the proposed approach 1 is robust and effective since it gives better results than the other matching algorithms under different ear challenges. The maximum accuracies achieved are 100% (under partial occlusion), 97.4% (under weak illumination), 100% (under pose variation), 97.5% (under real occlusion) for USTB-set1, USTB-set2, USTB-set3, UBEAR database, respectively. On the other hand, this study aims to measure the efficiency of ear and profile face modalities in human recognition under identification and verification modes. In order to obtain a robust multimodal recognition system using different feature extraction methods, 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 approach 2. Additionally, feature-level fusion is used for comparison. All experiments in this approach are implemented on the UBEAR database. Local Binary Patterns, Local Phase Quantization and Binarized Statistical Image Features approaches are used for feature extraction process in proposed approach 2. Images under different challenge such as illumination variation, pose variation and blurring are tested. Ear and profile face images from UBEAR database are used in the experiments. The experimental results show that the proposed approach 2 is more accurate and reliable than using ear or profile face images separately. The performance of the proposed approach 2 in terms of recognition rate is 100%, and in terms of Equal Error Rates is 1.9%.