Rotation invariant complex Zernike moments features and their applications to human face and character recognition

The magnitude of Zernike moments (ZMs) has been used as rotation invariant features for classification problems in the past. Their individual real and imaginary components and phase coefficients are ignored, because they change with rotation. This study presents a new method to modify the individual real and imaginary components of ZMs which change due to image rotation. The modified real and imaginary components are then used as invariant image descriptors. The performance of the proposed method and magnitude-based ZM method is analysed on grayscale face images and binary character images in application to the fields of face recognition and character recognition, respectively. Experimental results show that the proposed method is robust to image rotation. For classification, the authors use L₁-norm as the similarity measure. It is shown that the proposed method gives better recognition rate over the magnitude-based ZM method, comparatively at low orders of moment and thus it is recommended for pose invariant face recognition and also for rotation invariant character recognition. This has been proved by comparing the results of the proposed method with existing prominent methods of feature extraction in face and character recognition. On ORL database, the proposed method achieves the highest recognition rate of 96.5%, whereas a recognition rate of 99.7% is obtained on binary Roman character images.