Deep Learning Approach to Robust Face Recognition in Urban Environment


Deep Learning Approach to Robust Face Recognition in Urban Environment – We propose an effective and robust method for the face recognition task at hand. It extends the approach from the face recognition task of a user to that of a human. Our method uses a deep neural network to discover the region of interest and the global context of the region. Unlike previous approaches for recognizing faces in images, we focus on the region of interest in order to learn how to predict the identity function in the region. Our method learns the global context by learning a new face identity function that maps a set of the face instances together. We use this new identity function to predict the pose for a given face instance as well as pose prediction metrics such as SVMs. Our method outperforms state-of-the-art human-level face recognition methods on the BLEU dataset with an accuracy of 97% that is comparable to human experts, which are more challenging to achieve in the face recognition community.

In this paper, the task of image recognition based on LLE is presented. The goal of the task was to learn a discriminative LLE for image recognition. This is achieved by a hybrid learning scheme based on adaptive learning which combines adaptive sampling techniques. In this way, the discriminative LLE can achieve better performance than a generic LLE but is less accurate. In this paper, the task was to propose a novel discriminative model which is adaptive instead of adaptive with the aim of improving the accuracy of the LLE. To illustrate this idea, different models are proposed with different performance characteristics to the LLE model, including the adaptive learning method, adaptive sampling method, adaptive learning inversion method and adaptive learning inversion method. Experimental results on various benchmark datasets demonstrate that the proposed model improves the performance of the LLE recognition tasks compared to state-of-the-art models. Experimental results on a benchmark dataset of Chinese visual images show that the proposed discriminative model can perform better than the current state-of-the-art LLE.

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Deep Learning Approach to Robust Face Recognition in Urban Environment

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  • Online Optimization for Neural Network Training

    Improving the Accuracy of the LLE Using Multilayer PerceptronIn this paper, the task of image recognition based on LLE is presented. The goal of the task was to learn a discriminative LLE for image recognition. This is achieved by a hybrid learning scheme based on adaptive learning which combines adaptive sampling techniques. In this way, the discriminative LLE can achieve better performance than a generic LLE but is less accurate. In this paper, the task was to propose a novel discriminative model which is adaptive instead of adaptive with the aim of improving the accuracy of the LLE. To illustrate this idea, different models are proposed with different performance characteristics to the LLE model, including the adaptive learning method, adaptive sampling method, adaptive learning inversion method and adaptive learning inversion method. Experimental results on various benchmark datasets demonstrate that the proposed model improves the performance of the LLE recognition tasks compared to state-of-the-art models. Experimental results on a benchmark dataset of Chinese visual images show that the proposed discriminative model can perform better than the current state-of-the-art LLE.


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