One of the key issues in gait recognition is how to extract the low dimensional feature. Linear Discriminant Analysis (LDA) is a commonly used method for linear dimension reduction. This paper proposed a generalized LDA based on Euclidean norm (ELDA) for gait recognition. By redefining a better between-class scatter matrix to separate the neighboring samples that overcome the drawbacks existing in the traditional LDA method. Firstly, the contour is unwrapped counterclockwise by the distance from the uppermost pixel to transformed 2D features into 1D. Secondly, we use ELDA to obtain more discriminative feature space. Finally, multi-class Support Vector Machine (SVM) is applied to implement gait classification. Experimental results show that this algorithm achieves better results in terms of accuracy and efficiency than other gait recognition methods at present.

Multirobot task allocation is a hot issue in the field of robot research. A new emotional model is used with the self-interested robot, which gives a new way to measure self-interested robots' individual cooperative willingness in the problem of multirobot task allocation. Emotional cooperation factor is introduced into self-interested robot; it is updated based on emotional attenuation and external stimuli. Then a multirobot pursuit task allocation algorithm is proposed, which is based on emotional cooperation factor. Combined with the two-step auction algorithm recruiting team leaders and team collaborators, set up pursuit teams, and finally use certain strategies to complete the pursuit task. In order to verify the effectiveness of this algorithm, some comparing experiments have been done with the instantaneous greedy optimal auction algorithm; the results of experiments show that the total pursuit time and total team revenue can be optimized by using this algorithm.

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