In this paper we present a symmetry-based approach which can be used to detect humans and to extract biometric characteristics from video image-sequences. The method employs a simplified symmetry-feature extracted from the images. To obtain a useful descriptor of a walking person, we track temporally the symmetries which result from the movements of the person's legs. In a further processing stage these patterns are filtered, then re-sampled using Bezier-splines to generate an invariant and noise-cleaned signature or "feature". In our detection method the extracted spatio-temporal feature with a large number of dimensions (800) is transformed to a space with a much smaller number of dimensions (3), which we call the "eigenwalks space"; the method uses Principal Component Analysis (PCA) to reduce the dimensionality, and the Support Vector Machine (SVM) method in the eigenspace for recognition purposes. Finally we present a method by which we can estimate the gait-parameters (the beginning and end of a walk-cycle, identification of the leading leg) from the symmetry-patterns of the walking person, without camera calibration, based on two successive detected walk-steps.