3-parameter based eigenfeature regularization for human activity recognition

We propose an appearance based eigenfeature regularization methodology for recognizing human activities. This regularization utilizes a 3-parameter based eigenmodel derived from the variances of within-class (activity) scatter matrix. Original eigenvalues are replaced by the model eigenvalues which facilitates in regularizing eigenfeatures corresponding to very small and zero eigenvalues and perform discriminant evaluation in the whole eigenspace. This is done directly from the intensity information appearing in activity images. After this regularization, low dimensional discriminative features are extracted and used for recognizing various activities. Experimental results on two benchmark databases, Weizmann and INRIA-IXMAS show the superiority of our proposed approach over other popular methods.