This　paper　studies　the　problem　of　automatically　recognizing　human　eyebrows　using　a　frontal　view.　In　the　matching-recognizing　framework　for　image-based　object　classification,　we　design　an　automatic　human　eyebrow　recognition　system　via　fast　template　matching　and　Fourier　spectrum　distance.　Fast　template　matching　is　used　to　locate　the　target　subregion　of　a　gallery　template　or　a　pure　eyebrow　image　in　a　probe　original　eyebrow　image,　whereas　Fourier　spectrum　distance　is　used　to　determine　the　final　identity　of　the　probe　original　eyebrow　image.　We　conducted　a　number　of　experiments　to　demonstrate　the　efficacy　of　the　proposed　system　and　corroborate　the　validity　of　eyebrow　recognition　on　the　BJUT　eyebrow　database.　Moreover,　we　also　tested　the　system　on　the　color　FERET　database.　Experimental　results　show　that　our　approach　can　be　directly　applied　to　face　recognition　by　only　replacing　eyebrow　templates　with　face　templates,　and　may　achieve　higher　accuracy　in　eyebrow　recognition　than　in　small　face　recognition.　This　is　a　strong　argument　for　eyebrow　recognition　to　replace　face　recognition　as　an　independent　biometric　in　certain　scenarios,　especially　where　relatively　large　eyebrows　can　be　cropped.　(c)　2012　Elsevier　Inc.　All　rights　reserved.