and gait to recognize one another. This remarkable ability of human visual system led Professor Venetsanopoulos to build automated systems to recognize individuals from digitally captured facial images and gait sequences. Face and gait recognition belong to the field of biometrics, a very active area of research in computer science, mainly motivated by government and security-related considerations. Face and gait are two typical physiological and behavioral biometrics. Venetsanopoulos contributed to both areas and his research has been extensively cited. There are two general approaches to the subject: the appearance-based approach and the model-based approach. Appearance-based face recognition processes a 2-D facial image as 2-D holistic patterns. The whole face region is the raw input to a recognition system and each face image is commonly represented by a high-dimensional vector consisting of the pixel intensity values in the image. Thus, face recognition is transformed to a multivariate, statistical pattern recognition problem. In a similar fashion to appearance-based face recognition, an appearance-based gait recognition approach considers gait as a holistic pattern and uses a full-body representation of a human subject as silhouettes or contours. Gait video sequences are naturally three-dimensional objects, formally named tensor objects, and they are very difficult to deal with using traditional vector-based learning algorithms. In order to deal with these tensor objects effectively, Venetsanopoulos and his research team developed a framework of multilinear subspace learning, so that computation and memory demands are reduced, natural structure and correlation in the original data are preserved, and more compact and useful features can be obtained. The Model-based gait recognition approach considers a human subject as an articulated object, represented by various body poses. Professor Venetsanopoulos proposed a full-body, layered deformable model (LDM) inspired by the manually labeled body-part-level silhouettes. The LDM has a layered structure to model self-occlusion between body parts and it is deformable, so simple limb deformation is taken into consideration. In addition, it also models shoulder swing. The LDM parameters can be recovered from automatically extracted silhouettes and then used for recognition. Publications and grants Tas Venetsanopoulos authored or co-authored nine books; contributed chapters to 35 books; and published over 870 academic papers in refereed journals and conference proceedings. Venetsanopoulos' best known contributions to electrical engineering are: "Nonlinear Digital Filters: Principles and Applications", "Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications", "Color Image Processing and Applications", "WLAN Positioning Systems", "Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data".