In this study, haar-like features used for face detection on images and adaboost algorithm have been introduced, and an additional distinctive feature of traditional haar features have been proposed. The performances of recommended feature and traditional haar features in 'CBCL-MIT Center For Biological and Computation Learning' face database on the first 200 containing faces positive images and on the first 400 containing nonfaces negative pictures have been tested and the success rates (%) have been indicated in a table. As a result, the success rate of the supposed feature on face photos in the first three stages have been much higher than traditional haar features.