We present a new sparse shape modeling framework on the Laplace-Beltrami (LB) eigenfunctions. Traditionally, the LB-eigenfunctions are used as a basis for intrinsically representing surface shapes as a form of Fourier descriptors. To reduce high frequency noise, only the first few terms are used in the expansion and higher frequency terms are simply thrown away. However, some lower frequency terms may not necessarily contribute significantly in reconstructing the surfaces. Motivated by this simple idea, we present a LB-based method to filter out only the significant eigenfunctions by imposing an L1-norm penalty. The sparse surface shape model is applied in investigating the influence of age and gender on amygdala and hippocampus shapes in the normal population. This chapter is based on [205].