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Face recognition can significantly impact authentication, monitoring and indexing applications. Much research on face recognition using global and local information has been done earlier. By using global feature preservation techniques like principal component analysis (PCA) and linear discriminant analysis (LDA), the authors can effectively preserve only the Euclidean structure of face space that suffers lack of local features, but which may play a major role in some applications. On the other hand, the local feature preservation technique namely locality preserving projections (LPP) preserves local information and obtains a face subspace that best detects the essential face manifold structure; however, it also suffers loss in global features which may also be important in some of the applications. A new combined approach for recognising faces that integrates the advantages of the global feature extraction technique LDA and the local feature extraction technique LPP has been introduced here. Xiaofei He et al. in their work used PCA to extract similarity features from a given set of images followed by LPP. But in the proposed method, the authors use LDA (instead of PCA) to extract discriminating features that yields improved facial image recognition results. This has been verified by making a fair comparison with the existing methods.