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This work presents an automatic human gender and age group recognition systembased on human facial images. It makes an extensive experiment with row pixelintensity valued features and Discrete Cosine Transform (DCT) coefficientfeatures with Principal Component Analysis and k-Nearest Neighborclassification to identify the best recognition approach. The final resultsshow approaches using DCT coefficient outperform their counter parts resultingin a 99% correct gender recognition rate and 68% correct age group recognitionrate (considering four distinct age groups) in unseen test images. Detailedexperimental settings and obtained results are clearly presented and explainedin this report.

Age group and gender recognition from human facial images