We　propose　a　framework　of　hand　articulation　detection　from　a　monocular　depth　image　using　curvature　scale　space　(CSS)　descriptors.　We　extract　the　hand　contour　from　an　input　depth　image,　and　obtain　the　fingertips　and　finger-valleys　of　the　contour　using　the　local　extrema　of　a　modified　CSS　map　of　the　contour.　Then　we　recover　the　undetected　fingertips　according　to　the　local　change　of　depths　of　points　in　the　interior　of　the　contour.　Compared　with　traditional　appearance-based　approaches　using　either　angle　detectors　or　convex　hull　detectors,　the　modified　CSS　descriptor　extracts　the　fingertips　and　finger-valleys　more　precisely　since　it　is　more　robust　to　noisy　or　corrupted　data;　moreover,　the　local　extrema　of　depths　recover　the　fingertips　of　bending　fingers　well　while　traditional　appearance-based　approaches　hardly　work　without　matching　models　of　hands.　Experimental　results　show　that　our　method　captures　the　hand　articulations　more　precisely　compared　with　three　state-of-the-art　appearance-based　approaches.