Local Zernike Moment Representation for Facial Affect Recognition

In this paper, we propose to use local Zernike Moments (ZMs) for facial affect recognition and introduce a representation scheme based on performing non-linear encoding on ZMs via quantization. Local ZMs provide a useful and compact description of image discontinuities and texture. We demonstrate the use of this ZM-based representation for posed and discrete as well as naturalistic and continuous affect recognition on standard datasets, and show that ZM-based representations outperform well-established alternative approaches for both tasks. To the best of our knowledge, the performance we achieved on CK+ dataset is superior to all results reported to date.