Facial aging and asymmetry decomposition based approaches to identification of twins

A reliable and accurate biometric identification system must be able to distinguish individuals even in situations where their biometric signatures are very similar. However, the strong similarity in the facial appearance of twins has complicated facial feature based recognition and has even compromised commercial face recognition systems. This paper addresses the above problem and proposes two novel methods to distinguish identical twins using (1) facial aging features and (2) asymmetry decomposition features. Facial aging features are extracted using Gabor filters from regions of the face that typically exhibit wrinkles and laugh lines, while Facial asymmetry decomposition based features are obtained by projecting the difference between the two left sides (consisting of the left half of the face and its mirror) and two right sides (consisting of the right half of the face and its mirror) of a face onto a subspace. Feature vectors obtained using these methods were used for classification. Experiments conducted on images of five types of twins from the University of Notre Dame ND-Twins database indicate that both our proposed approaches achieve high identification rates and are hence quite promising at distinguishing twins.