Face recognition using complex wavelet moments

The wavelet moments are very useful image descriptors which contain both global and local characteristics of an image. Its usefulness is further characterized by rotation invariant property of its magnitude. The phase information is, however, left out because it changes with image rotation. In this paper, we incorporate phase information and use both the real and imaginary components of wavelet moments to describe an image and develop a similarity measure which is invariant under image rotation. The proposed framework is applied to face recognition. Extensive experimental results are provided to demonstrate the enhanced performance of the proposed framework as compared to the magnitude only framework. The results are also compared with many other state-of-the-art techniques used for face recognition.

► The new similarity measure involves both magnitude and phase of wavelet moments (WMs).
► The similarity measure compares real and imaginary parts of WMs of two images.
► The number of features is twice to that of magnitude only WMs.
► The face recognition rate is enhanced.