High false positive face detection is a crucial problem which leads to low performance face recognition in surveillance system. The performance can be increased by reducing these false positives so that non-face can be discarded first prior to recognition. This paper presents a combination of two well-known algorithms, Adaboost and Neural Network, to detect face in static images which is able to reduce the false-positives drastically. This method utilizes Haar-like features to extract the face rapidly using integral image. A cascade Adaboost classifier is used to increase the face detection speed. Due to using only this cascade Adaboost produces high false-positives, neural network is used as the final classifier to verify face or non-face. For a faster processing time, hierarchical Neural Network is used to increase the face detection rate. Experiments on four different face databases, which consist more than one thousand images, have been conducted. Results reveal that the proposed method achieves about 93.34% of detection rate and 0.34% of false positives compared to original cascade Adaboost method which achieves about 98.13% of detection rate with 6.50% of false positives. The processed images size is 240 x 320 pixels. Each frame is processed at about 2.25 sec which is slights lower than the original method, which only takes about 0.82 sec.