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.

In this article, an important question that concerns how to use word-level representations in machine translation is considered. The task is to discover the best sentence that can encode a given word for each word in the language’s context. Given a sentence and a set of sentences, a word-level representation has two functions. A word encoder is learned to encode the word’s meaning in the context. A word-level encoder is inferred to encode the sentence in the context. In the case of word-level models, a word-level encoding is also learned to produce the sentence in the context. This knowledge is used as a prior for subsequent inference, so that new words of the given sentence can be learned. The proposed model is evaluated using English-Urdu translation and a French-Urdu translation. The experiments show that the model can reach a better result with fewer parameters.

Evaluation of a Multilayer Weighted Gaussian Process Latent Variable Model for Pattern Recognition

Face Detection from Multiple Moving Targets via Single-Path Sampling

On the Relation between Human Image and Face Recognition

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  • Fast k-Nearest Neighbor with Bayesian Information Learning

    A Deep Learning Approach to Extracting Plausible Explanations From Chinese Handwriting TextsIn this article, an important question that concerns how to use word-level representations in machine translation is considered. The task is to discover the best sentence that can encode a given word for each word in the language’s context. Given a sentence and a set of sentences, a word-level representation has two functions. A word encoder is learned to encode the word’s meaning in the context. A word-level encoder is inferred to encode the sentence in the context. In the case of word-level models, a word-level encoding is also learned to produce the sentence in the context. This knowledge is used as a prior for subsequent inference, so that new words of the given sentence can be learned. The proposed model is evaluated using English-Urdu translation and a French-Urdu translation. The experiments show that the model can reach a better result with fewer parameters.


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