Distinguishing Identical Twins Using Facial Images and Various Feature Extractors

Recognizing identical twins is considered as one of the most critical challenges in biometric systems due to the shortage of uniqueness and distinction between the identical twins. The lack of discriminative features could be compensated using different sources of information. In this thesis, two different hybrid approaches using three biometric traits namely frontal face, profile face and ear are proposed and implemented to distinguish identical twins. The proposed strategies are particularly based on feature-level fusion, score-level fusion and decision-level fusion. Both proposed approaches are evaluated using identical twins and non-twins individuals. In the proposed method 1, frontal face is employed together with three feature extraction algorithms namely Principal Component Analysis, Histogram of Oriented Gradients and Local Binary Patterns. Fusion in this approach is conducted by all the aforementioned fusion techniques and different challenges are considered such as illumination, expression and ageing using ND-Twins-2009-2010 and FERET databases. The lowest Equal Error Rates of identical twins recognition that are achieved using the proposed method are 2.07% for natural expression, 0.0% for smiling expression and 2.2% for controlled illumination compared to 4.5%, 4.2% and 4.7% Equal Error Rates of the best state-of-the-art algorithm under the same conditions. On the other hand, symmetry challenge of profile face and ear is tested in the proposed approach 2 by using Local Binary Patterns, Local Phase Quantization and Binarized Statistical Image Features feature extraction algorithms. The samples of both sides of profile face and ear are extracted from ND-Twins-2009-2010 and UBEAR databases. In this approach, the extent of symmetry of left and right sides of each trait is measured in order to be used for recognition purposes. Finally, symmetry experiments using multimodal biometric traits are implemented and compared with our proposed approach which uses feature-level and score-level fusion. The maximum accuracies achieved are 75% for identical twins using ND-Twins-2009- 2010 database; moreover 88.04% and 79.89% for non-twins using ND-Twins-2009- 2010 and UBEAR databases, respectively. Keywords: identical twins, face recognition, ear recognition, score-level fusion, feature-level fusion, decision-level fusion, multimodal biometrics.