Projected Texture for Object Classification Avinash Sharma and Anoop Namboodiri Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, INDIA - 500 032 Abstract. Algorithms for classification of 3D objects either recover the depth information lost during imaging using multiple images, structured lighting, image cues, etc. or work directly the images for classification. While the latter class of algorithms are more efficient and robust in comparison, they are less accurate due to the lack of depth informa- tion. We propose the use of structured lighting patterns projected on the object, which gets deformed according to the shape of the object. Since our goal is object classification and not shape recovery, we char- acterize the deformations using simple texture measures, thus avoiding the error prone and computationally expensive step of depth recovery. Moreover, since the deformations encode depth variations of the object, the 3D shape information is implicitly used for classification. We show that the information thus derived can significantly improve the accuracy of object classification algorithms, and derive the theoretical limits on height variations that can be captured by a particular projector-camera setup. A 3D texture classification algorithm derived from the proposed approach achieves a ten-fold reduction in error rate on a dataset of 30 classes, when compared to state-of-the-art image based approaches. We also demonstrate the effectiveness of the approach for a hand geometry based authentication system, which achieves a four-fold reduction in the equal error rate on a dataset containing 149 users. 1 Introduction Three dimensional object are characterized by their shape, which can be thought of as the variation in depth over the object, from a particular view point. These variations could be deterministic as in the case of rigid objects or stochastic for surfaces containing a 3D texture. The depth information are lost during the process of imaging and what remains is the intensity variations that are induced by the object shape and lighting, as well as focus variations. Algorithms that utilize 3D object shape for classification tries to recover the lost depth informa- tion from the intensity or focus variations or using additional cues from multiple images, structured lighting, etc. This process is computationally intensive and error prone. Once the depth information is estimated, one needs to characterize the object using shape descriptors for the purpose of classification. Image-based classification algorithms tries to characterize the intensity vari- ations of the image of the object for recognition. As we noted, the intensity D. Forsyth, P. Torr, and A. Zisserman (Eds.): ECCV 2008, Part III, LNCS 5304, pp. 616–627, 2008. c  Springer-Verlag Berlin Heidelberg 2008