Human face detection plays an important role in many application areas such as video surveillance, human computer interface, face recognition, face search and face image database management etc. In human face detection applications, face region usually form an inconsequential part of images. Preliminary segmentation of images into regions that contain "non-face" objects and regions that may contain "face" can greatly accelerate the process of human face detection. This can be done using skin color segmentation, where given image is segmented based on color as 'skin region' and 'non skin regions'. Thus we can say that the skin regions may contain face and other regions don't. Color information based methods take a great attention, because colors have obviously character and robust visual cue for detection. This paper proposes a method based on RGB color centroids segmentation (CCS) for face detection. This paper includes two parts, first part is color image thresholding based on CCS to perform skin color segmentation and the then detection of human face from detected skin regions. CCS method has some shortcomings as it fails when the skin color of the subject lacks chroma. This happens especially with subjects having too darker or too lighter skin tones. This shortcoming of CCS can be overcome using Contourlet Transformation. In this paper, we pursue a two dimensional transform that can capture the intrinsic geometrical structure that is key in visual information.