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Facial asymmetry has now been established as a useful biometric for human identification in the presence of expression variations ([1]). The current paper investigates an alternative representation of asymmetry in the frequency domain framework, and its significance in identification tasks in terms of the phase component of the frequency spectrum of an image. The importance of the latter in face reconstruction is well-known in the engineering literature ([2]) and this establishes a firm ground for the success of asymmetry as a potentially useful biometric. We also point out some useful implications of this connection and dual representation. Moreover, the frequency domain features are shown to be more robust to intra-personal distortions than the corresponding spatial measures and yield error rates as low as 4% on a dataset with images showing extreme expression variations.

Facial Asymmetry in Frequency Domain: The “Phase” Connection