GA-based optimal selection of PZMI features for face recognition

One of the key problems in automated face recognition system is that of handling the face image variation in terms of scale, rotation (in plane) and translation. One approach is fixing mentioned problems in recognition processes by extracting one linear transformation invariant feature. This paper presents a novel method for face recognition. Pseudo Zernike moment invariant (PZMI) which has linear transformation invariance properties and is robust in the presence of noise utilized to produce feature vectors. For decreasing computational complexity of feature extraction step, we use genetic algorithm (GA) to select the optimal feature set which contains optimal PZMI orders and corresponding repetitions. In addition, we have investigated the effect of PZMI orders on recognition rate in noisy images. Proposed scheme has been tested on the FERET database. Experimental results prove the advantages of the proposed method when compared with other PZMI-based face recognition systems.