LSTM with Multi-dimensional Generative Adversarial Networks for Facial Action Unit Recognition


LSTM with Multi-dimensional Generative Adversarial Networks for Facial Action Unit Recognition – Traditional face detectors mainly rely on hand-written or hand-drawn sketches for detecting facial expressions. However, human models usually are not fully developed yet, so they may not be able to be used for facial expressions on a large scale. Here, we propose a non-stationary face detector based on deep Convolutional Networks (CNNs) for face detection with the goal of fully integrating them. Since CNNs allow us to model faces in images, our network aims to extract features from image images by maximizing the CNN’s ability to capture facial features for each pixel. We propose Deep-CNNs that can learn a non-stationary model that captures more detail than the one that does capture any single pixel of image. To show that our network achieves better accuracy than CNNs, we have used an image segmentation and face recognition model under various conditions. To the best of our knowledge, this is the first time we have used a CNN for face detection under such conditions. In a similar way, we also show that human model can be used to model human behavior under different conditions.

This paper describes a general network architecture for the diagnosis of Alzheimer’s Disease (AD) by measuring the influence of several different diseases’ components in cognitive aging. In cognitive aging, aging processes progressively increase in severity and the overall health status of an individual, leading to an increase in the number of patients and their mortality. Moreover, due to the complex nature of aging process, and increasing complexity of the Alzheimer’s disease (AD) process, the medical and research community need rapid information on age assessment to better understand the effects of Alzheimer’s disease and to make timely decisions for the well-being of patients.

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LSTM with Multi-dimensional Generative Adversarial Networks for Facial Action Unit Recognition

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    A statistical model of aging in the neuroimaging fieldThis paper describes a general network architecture for the diagnosis of Alzheimer’s Disease (AD) by measuring the influence of several different diseases’ components in cognitive aging. In cognitive aging, aging processes progressively increase in severity and the overall health status of an individual, leading to an increase in the number of patients and their mortality. Moreover, due to the complex nature of aging process, and increasing complexity of the Alzheimer’s disease (AD) process, the medical and research community need rapid information on age assessment to better understand the effects of Alzheimer’s disease and to make timely decisions for the well-being of patients.


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