face image recognition based on joint features

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The basic idea is as follows. We first train the joint feature learning model on small face patches and then use the learned feature weighting matrices to filter sampled small patches within large patches.2 Within each large image patch, the outputs of small patches are concatenated and taken as the input of the next layer. We employ weighted PCA (WPCA) [38] to map the combined responses of the first layer to a low-dimensional feature space to reduce the redundancy. Similarly to most deep learning methods [29], [34], [35], [50], the stacked joint feature learning network is trained greedily layer-wise. Specifically, we train the first layer until convergence before training the second layer. Having learned feature extraction matrices for the first and second layers, we project samples from each layer with the learned feature projections and learn dominant patterns to generate the codebook for each region and layer, respectively. In our experiments, we combine features extracted from both layers and combine them together for face recognition. In the experiment section, we show that this combination works better than using feature extracted from a single individual layer.

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