A fast face detection algorithm using enhanced AdaBoost based on Walsh features is proposed in this paper, and its training process is fast and works well under fewer non-face training samples.Firstly,the utility of Walsh features, instead of Harr-Like features can reduce the redundancy among features largely. Then, an enhanced double threshold AdaBoost algorithm is developed, where double threshold makes training process faster ; and in the process of training cascaded detector, the next classifier can be guided by the former classifier,which enhances the performance of the cascaded detector ;moreover,the adjustment to the threshold of each classifier can separate the training result of face and on-face as far as possible. Finally, the trained detector is tested on MIT + CMU test set, and experimental results show that its training speed, precision and detection time exceeds the corresponding method.