Analyzing asymmetry biometric in the frequency domain for face recognition
The paper introduces a novel set of facial biometrics based on quantified facial asymmetry measures in the frequency domain. In particular, we show that these biometrics work well for images showing expression variations. A comparison of the recognition rates with those obtained from spatial domain asymmetry measures based on raw intensity values suggests that the frequency domain representation is more robust to intra-personal distortions and, indeed, provides an efficient approach for performing classification or recognition. The role of asymmetry of the different regions (e.g., eyes, mouth, nose) of the face is investigated to determine which regions provide the maximum discrimination among individuals in the presence of different expressions for better classification results in such a scenario.