Orthogonal moments are used to represent digital images with minimum
redundancy. Orthogonal moments with fractional-orders show better capabilities in
digital image analysis than integer-order moments. In this work, the authors present
new fractional-order shifted Gegenbauer polynomials. These new polynomials are
used to defined a novel set of orthogonal fractional-order shifted Gegenbauer moments
(FrSGMs). The proposed method is applied in gray-scale image analysis and
recognition. The invariances to rotation, scaling and translation (RST), are achieved
using invariant fractional-order geometric moments. Experiments are conducted to
evaluate the proposed FrSGMs and compare with the classical orthogonal integerorder
Gegenbauer moments (GMs) and the existing orthogonal fractional-order
moments. The new FrSGMs outperformed GMs and the existing orthogonal
fractional-order moments in terms of image recognition and reconstruction, RST
invariance, and robustness to noise.