A hybrid technique based on facial feature extraction and Principal Component Analysis (PCA) is presented for frontal face detection in color images. Facial features such as eyes and mouth are automatically detected based on properties of the associated image regions, which are extracted by RSST color segmentation. While mouth feature points are identified using the redness property of regions, a simple search strategy relative to the position of the mouth is carried out to identify eye feature points from a set of regions. Priority is given to regions which signal high intensity variance, thereby allowing the most probable eye regions to be selected. On detecting a mouth and two eyes, a face verification step based on Eigenface theory is applied to a normalized search space in the image relative to the distance between the eye feature points.