A novel discriminant criterion based on feature fusion strategy for face recognition

Feature extraction is an important problem in face recognition. There are two kinds of structural features, namely the Euclidean structure and the manifold structure. However, the single-structural feature extraction methods cannot fully utilize the advantages of global feature and local feature simultaneously. Thus their performances will be degraded. To overcome the limitations of the single-structural feature based face recognition schemes, this paper proposes a novel discriminant criterion using Feature Fusion Strategy (FFS), which nonlinearly combines both Euclidean and manifold structures in the face pattern space. The proposed discriminant criterion is suitable to develop an iterative algorithm. It is able to automatically determine the optimal parameters and balance the tradeoff between Euclidean structure and manifold structure. The proposed FFS algorithm is successfully applied to face recognition. Three publicly available face databases, ORL, FERET and CMU PIE, are selected for evaluation. Compared with Linear Discriminant Analysis (LDA), Locality Preserving Projection (LPP), Unsupervised Discriminant Projection (UDP) and Semi-Supervised LDA (SSLDA), the experimental results show that the proposed method gives superior performance.