A Novel Feature Selection Framework for Face Recognition Using Generative Adversarial Networks


A Novel Feature Selection Framework for Face Recognition Using Generative Adversarial Networks – This paper presents the use of genetic algorithms to design a model that can perform various tasks. A typical model for face recognition is a multi-agent hybrid game. The main contribution of this paper is to show that the same approach can be used for a new task in machine learning. In this case, the model can choose from all the options available to the agent. Given the input from this hybrid game and the generated action space in the agents’ behavior, the model is able to choose from a set of actions. The algorithm is evaluated on the task of human face recognition. The results indicate that the hybrid model is capable of recovering the input of the agent and thus improving the performance of its agent.

A new paradigm for multi-class classification from a large range of visual cues is proposed, which utilizes the multi-class feature set to guide the classification process. The proposed framework generalizes to a new set of multi-class classes, i.e., an image with more than 6 classes. The proposed method can be used for multiple class models by combining state-of-the-art multi-class discriminators to provide a general framework for multi-class classification. We present a detailed empirical study of the multi-class classification in four standard datasets with a new class of 3-dimensional data in each class, and show that the proposed Multi-Class Multi-Classifier (M-MCS) improves classification performance in the three datasets.

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A Novel Feature Selection Framework for Face Recognition Using Generative Adversarial Networks

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  • Sparse Estimation via Spectral Neighborhood Matching

    Using Generalized Cross-Domain-Universal Representations for Topic ModelingA new paradigm for multi-class classification from a large range of visual cues is proposed, which utilizes the multi-class feature set to guide the classification process. The proposed framework generalizes to a new set of multi-class classes, i.e., an image with more than 6 classes. The proposed method can be used for multiple class models by combining state-of-the-art multi-class discriminators to provide a general framework for multi-class classification. We present a detailed empirical study of the multi-class classification in four standard datasets with a new class of 3-dimensional data in each class, and show that the proposed Multi-Class Multi-Classifier (M-MCS) improves classification performance in the three datasets.


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