Generalized linear discriminant analysis based on euclidean norm for gait recognition

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.