A　gender　classification　system　uses　human　face　from　a　given　image　to　tell　the　gender　of　the　given　person.　An　effective　gender　classification　approach　is　able　to　promote　the　improvement　of　many　other　applications,　including　image/video　retrieval,　security　monitor,　human-computer　interaction,　etc.　In　this　paper,　a　method　for　gender　classification　task　in　frontal　face　images　based　on　stacked-autoencoders　is　proposed.　Firstly,　gender　features　are　learned　from　frontal　face　images,　followed　by　dimensionality　reduction　with　stacked-autoencoders　algorithm　with　fine-tuning　strategy,　which　serves　as　the　feature　vectors　of　our　method.　Ultimately,　two　kinds　of　classifiers,　SVM　and　Softmax　regression,　are　trained　to　the　task　of　classification.　The　experiment　on　FERET　and　CAS-PEAL-R1　face　datasets　is　reported　that　an　effective　method　is　proposed　for　gender　classification　task　and　other　methods　are　compared　with　ours.　©　2014　IEEE.