  This work presents an automatic human gender and age group recognition system
based on human facial images. It makes an extensive experiment with row pixel
intensity valued features and Discrete Cosine Transform (DCT) coefficient
features with Principal Component Analysis and k-Nearest Neighbor
classification to identify the best recognition approach. The final results
show approaches using DCT coefficient outperform their counter parts resulting
in a 99% correct gender recognition rate and 68% correct age group recognition
rate (considering four distinct age groups) in unseen test images. Detailed
experimental settings and obtained results are clearly presented and explained
in this report.
