Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition

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A Multi-Agent Framework for Packet Routing in Wireless Sensor Networks (2015, background): “problems, such as cliff-walking [30], to more complex ones, such as web-based education [31] and face recognition [32].”
Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching (2011, background): “While the approaches presented in [13, 26] show promising results, the conventional formalism of DA suffers from not being able to take into account the local structure of data [10, 15].”
Dynamic analysis of multiagent Q-learning with ε-greedy exploration (2009, methodology): “Q-learning is certainly one of the most studied Reinforcement Learning (RL) algorithm and has been applied with success in several domains, from relatively simple toy problems, such as Cliff-Walking (Sutton & Barto, 1998), to more complex ones, such as web-based education (Iglesias et al., 2008) and”
Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition (2011, background): “This results in issues for face recognition such as low resolution, blurry images (due to motion or loss of focus), large pose variations, and low contrast [14, 35, 40].”