Person-independent expression recognition based on person-similarity weighted expression feature

A new method to extract person-independent expression feature based on higher-order singular value decomposition （HOSVD） is proposed for facial expression recognition. Based on the assumption that similar persons have similar facial expression appearance and shape, the person-similarity weighted expression feature is proposed to estimate the expression feature of test persons. As a result, the estimated expression feature can reduce the influence of individuals caused by insufficient training data, and hence become less person-dependent. The proposed method is tested on Cohn-Kanade facial expression database and Japanese female facial expression （JAFFE） database. Person-independent experimental results show the superiority of the proposed method over the existing methods.

A Deep Reinforcement Learning Based Car Following Model for Electric Vehicle

Car following (CF) models are an appealing research area because they fundamentally describe longitudinal interactions of vehicles on the road, and contribute significantly to an understanding of traffic flow. There is an emerging trend to use data-driven method to build CF models. One challenge to the data-driven CF models is their capability to achieve optimal longitudinal driven behavior because a lot of bad driving behaviors will be learnt from human drivers by the supervised learning manner. In this study, by utilizing the deep reinforcement learning (DRL) techniques trust region policy optimization (TRPO), a DRL based CF model for electric vehicle (EV) is built. The proposed CF model can learn optimal driving behavior by itself in simulation. The experiments on following standard driving cycle show that the DRL model outperforms the traditional CF model in terms of electricity consumption.

NEW ICA ALGORITHMS BASED ON SPECIAL LINEAR GROUP

Unlike the traditional independent component analysis(ICA)algorithms and some recently emerging linear ICA algorithms that search for solutions in the space of general matrices or orthogonal matrices,in this paper we propose two new methods which only search for solutions in the space of the matrices with unitary determinant and without whitening.The new algorithms are based on the special linear group SL(n).In order to achieve our target,we first provide a representation theory for any matrix in SL(n),which only simply uses the product of multiple exponentials of traceless matrices.Based on the matrix representation theory,two novel ICA algorithms are developed along with simple analysis on their equilibrium points.Moreover,we apply our methods to the classical problem of signal separation.The experimental results indicate that the superior convergence of our proposed algorithms,which can be expected as two viable alternatives to the ICA algorithms available in publications.