Models for Age Invariant Face Recognition
Aging variation poses one of the major problems to automatic face recognition systems. Most of the face recognition studies that have addressed the aging problem have focused on age estimation or aging simulation. However, research on age invariant face recognition is limited. Designing an appropriate feature representation and an effective matching framework for age invariant face recognition remains an open problem. In this paper, we propose a discriminative model to address face matching in the presence of age variation. In this framework, we first represent each face using two patch-based local feature representations, one based on scale invariant feature transform (SIFT) and the other based on multi-scale local binary patterns (MLBP). Since both SIFT-based features and MLBP-based features span a high-dimensional feature space, to reduce the feature dimensionality and avoid the over fitting problem, we use multi-feature discriminant analysis (MFDA) to process these two local feature spaces in a unified framework. The MFDA integrates two different random sampling techniques (random subspace and bagging) to improve the performance of linear discriminant analysis (LDA). By random sampling the training set as well as the feature space, several LDA-based classifiers are constructed and then combined to generate a robust decision via a fusion rule. Experimental results show that our approach outperforms a state-of-the-art commercial face recognition engine on MORPH, a large-scale public domain face aging dataset. We also compare the performance of the proposed discriminative model with a generative aging model. A fusion of discriminative and generative models further improves the face identification accuracy in the presence of aging.
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