Random projection-based partial feature extraction for robust face recognition

In this paper, a novel feature extraction method for robust face recognition (FR) is proposed. The proposed method combines a simple yet effective dimensionality increasing (DI) method with an information-preserving dimensionality reduction (DR) method. For the proposed DI method, we employ the rectangle filters which sum the pixel values within a randomized rectangle window on the face image to extract the feature. By convolving the face image with all possible rectangle filters having various locations and scales, the face image in the image space is projected to a very high-dimensional feature space where more discriminative information can be incorporated. In order to significantly reduce the computational complexity while preserving the most informative features, we adopt a random projection method based on the compressed sensing theory for DR. Unlike the traditional holistic-based feature extraction methods requiring the time-consuming data-dependent training procedure, the proposed method has the partial-based and data-independent properties. Extensive experimental results on representative FR databases show that, as compared with conventional feature extraction methods, our proposed method not only achieves the higher recognition accuracy but also shows better robustness to corruption, occlusion, and disguise.