Performance comparison of min-max normalisation on frontal face detection using haar classifiers

Face detection and analysis is an important area in computer vision. furthermore,face detection has been an active research field in the recent years following the advancement in digital image processing. the visualisation of visual entities or sub-pattern composition may become complex to visualise due to the high frequency of noise and light effect during examination. this study focuses on evaluating the ability of haar classifier in detecting faces from three paired min-max values used on histogram stretching. min-max histogram stretching was the selected method for implementation given that it appears to be the appropriate technique from the observation carried out. experimental results show that,60-240 min- max values,haar classifier can accurately detect faces compared to the two values. © 2017 universiti putra malaysia press.