MorphMan: A System for Morph Recognition


MorphMan: A System for Morph Recognition – To better understand how such a system works, we firstly explore the problem of learning to perform MorphMan with an adaptive learning approach based on the recognition of morphological patterns from the input data. We develop the first model trained to learn MorphMan, by proposing an algorithm of learning an adaptive learning algorithm that learns a morphological model using its data. We show that the adaptive learning algorithm is able to recognize morphological patterns that are similar to the output of the MorphMan algorithm, in the sense that the learned model has a common representation and a common morphological form. Experiments on real-world morphological data have shown that our approach is superior to the state of the art.

In this paper, we propose a supervised learning algorithm for a novel 3D facial segmentation problem. On average two trained models with identical facial segmentation output from different cameras are combined into one 3D. The training model is the single-camera model and our goal is to maximize the segmentation performance of the model. The 3D system is trained on an image that exhibits the appearance and color-level of a human hand. At the end of the training stage the extracted segmentation results are compared to a single-camera model. The trained model has different features compared to a single-camera model using a convolutional neural network (CNN). Experimental evaluation shows that our method compares favorably to state-of-the-art 3D segmentation algorithms where both models have similar performance. We show that our algorithm is effective and efficient on a variety of facial segmentation benchmarks. Finally, in the face verification domain, our algorithm achieves a new state-of-the-art 2.4D recognition accuracy.

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MorphMan: A System for Morph Recognition

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  • Learning Class-imbalanced Logical Rules with Bayesian Networks

    Face Recognition: Fusing Feature and Image from Camera StackoverflowIn this paper, we propose a supervised learning algorithm for a novel 3D facial segmentation problem. On average two trained models with identical facial segmentation output from different cameras are combined into one 3D. The training model is the single-camera model and our goal is to maximize the segmentation performance of the model. The 3D system is trained on an image that exhibits the appearance and color-level of a human hand. At the end of the training stage the extracted segmentation results are compared to a single-camera model. The trained model has different features compared to a single-camera model using a convolutional neural network (CNN). Experimental evaluation shows that our method compares favorably to state-of-the-art 3D segmentation algorithms where both models have similar performance. We show that our algorithm is effective and efficient on a variety of facial segmentation benchmarks. Finally, in the face verification domain, our algorithm achieves a new state-of-the-art 2.4D recognition accuracy.


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