On the Relation between Human Image and Face Recognition

On the Relation between Human Image and Face Recognition – We present a new method for extracting human faces from facial data of different human facial expressions. Our method is based on convolutional neural networks, which consists of recurrent layers to encode the human face state, then the convolution layers to learn the discriminative feature maps. We show that convnets with the learned features encode the human facial expression representations significantly better and achieve state-of-the-art performance on a face recognition task.

We present a novel technique for learning low-probability, unsupervised classifiers for motion from a single, annotated image. Our method is based on the concept of subspace learning, where the learning objective is to learn an appropriate set of labels for each pixel, which are useful for classifying objects. By combining a sparse set of labels, our approach generalises well, which is a key requirement in many state-of-the-art classifiers for motion. We evaluated our method on a range of simulated and real world datasets and outperformed the state-of-the-art models on both synthetic and real datasets.

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On the Relation between Human Image and Face Recognition

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  • Dyadic Submodular Maximization

    Unsupervised learning of motionWe present a novel technique for learning low-probability, unsupervised classifiers for motion from a single, annotated image. Our method is based on the concept of subspace learning, where the learning objective is to learn an appropriate set of labels for each pixel, which are useful for classifying objects. By combining a sparse set of labels, our approach generalises well, which is a key requirement in many state-of-the-art classifiers for motion. We evaluated our method on a range of simulated and real world datasets and outperformed the state-of-the-art models on both synthetic and real datasets.


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