Hierarchical face recognition using color and depth information


Hierarchical face recognition using color and depth information – In this paper, we propose a deep attention-based semantic segmentation framework for face recognition. Specifically, we propose the framework, which is able to capture both semantic and geometric information of faces using an attention-based semantic segmentation model. Our framework is based on the recent contribution of Bhattacharyya and others to our work.

We propose an adaptive algorithm for predicting the next segment of a video. The algorithm is based on a deep neural network that learns the pose of object in a scene for each image frame. A network is designed so that each image frame is a set of frames. The algorithm is applied on videos of a movie scene, and used to predict whether an object is in the scene. We demonstrate that the algorithm can be a very effective approach for learning the pose of objects. We evaluate the performance with the goal of determining the pose of all objects in the video.

Machine Learning for the Classification of High Dimensional Data With Partial Inference

DeepFace2Face: A Fully Convolutional Neural Network for Real-Time Face Recognition

Hierarchical face recognition using color and depth information

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  • Learning Stereo Visual Odometry using Restricted Boltzmann Machines

    Recurrent Inference with Adaptive Submodular FunctionsWe propose an adaptive algorithm for predicting the next segment of a video. The algorithm is based on a deep neural network that learns the pose of object in a scene for each image frame. A network is designed so that each image frame is a set of frames. The algorithm is applied on videos of a movie scene, and used to predict whether an object is in the scene. We demonstrate that the algorithm can be a very effective approach for learning the pose of objects. We evaluate the performance with the goal of determining the pose of all objects in the video.


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