A distinctive landmark-based face recognition system for identical twins by extracting novel weighted features

Abstract

High similarity in facial appearances of twins has complicated the facial feature-based recognition task. This paper introduces a Distinctive Landmark-based Face Recognition (DLFR) system to mitigate the problem. Novel features are proposed based on the number of key points obtained from a modified scale-invariant feature transform algorithm and also the most distinctive landmark region of the face. A slightly altered genetic algorithm is used to optimize weights for the features. The weighted features are classified using a support vector machine classifier. Extensive experiments on 440 face images of identical twins and non-twin individuals show the higher performance of the DLFR system in comparison to a previous work in distinguishing between identical twins and individuals with similar facial images. Because of substantial differences of the DLFR system with most conventional face recognition systems, the work could motivate the research community to push forward the embodied novel idea further.