Sharif University of Technology Scientia Iranica 1026-3098 22 6 2015 12 01 A fast face detection method for illumination variant condition A fast face detection method for illumination variant condition 2081 2091 3756 EN C.-H. Hsia Department of Electrical Engineering, Chinese Culture University, Taipei, Taiwan J.-S. Chiang Department of Electrical Engineering, Tamkang University, New Taipei, Taiwan C.-Y. Lin Department of Electrical Engineering, Tamkang University, New Taipei, Taiwan Journal Article 2016 01 02 General boosting algorithms for face detection use rectangular features. To obtain a better performance, it needs more training samples and may generate an unpredictable number of features. Besides using pixel values, which are easily aected by illumination, to calculate the rectangular features, it usually needs to preprocess the data before calculating the values of the features. Such an approach may increase computation time. To overcome the drawbacks, we propose a new solution based on the Adaboost algorithm and the Back Propagation Network (BPN) of a Neural Network (NN), combining local and global features with cascade architecture to detect human faces. We use the Modied Census Transform (MCT) feature, which belongs to texture features and is less sensitive to illumination, for local feature calculation. In this approach, it is not necessary to preprocess each sub-window of the image. For classication, we use the structure of the hierarchical feature to control the number of features. With only MCT, it is easy to misjudge faces and, therefore, in this work, we include the brightness information of global features to eliminate the False Positive (FP) regions. As a result, the proposed approach can have a Detection Rate (DR) of 99%, an FPs of only 11, and detection speed of 27.92 Frames Per Second (FPS). https://scientiairanica.sharif.edu/article_3756_1fa54ffe927da5a4d11ad735b254cc5a.pdf