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.