In contrast to global image descriptors which compute features directly from the entire image, local descriptors representing the features in small local image patches have proved to be more effective in real world conditions. This paper considers three recent yet popular local descriptors, namely Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Binarized Statistical Image Features (BSIF), and provides extensive comparative analysis on two different research problems (gender and texture classification) using benchmark datasets. The three descriptors are analyzed in terms of both classification accuracy and computational costs. Furthermore, experiments on combining these descriptors are provided, pointing out useful insight into their complementarity.