Face Detection Using Haar Cascade Classifiers Based on Vertical Component Calibration

The growing significance of the security and human management fields attracts active research related to face detection and recognition systems. Among these face detection techniques based on machine learning, Haar cascade classifiers are widely used because of their high accuracy for human frontal faces. However, the Haar cascade classifiers have a limitation in that the processing time increases as the number of false positives increases because they detect human faces based on the sub-window operation. Therefore, in this paper, a preprocessing method based on a 2D Haar discrete wavelet transform is proposed for face detection. The proposed method improves the processing speed by reducing the number of false positives through a vertical component calibration process using the vertical and horizontal components. The results of the face detection experiments that use a public test dataset comprising 2,845 images showed that the proposed method improved the processing speed by 32.05% and reduced the number of false positives by 25.46%, compared with those of the histogram equalization that shows the best performance case among conventional filter-based pre-processing methods. In addition, the performance of the proposed method is similar to those of conventional image contraction-based methods. In an experiment using a private dataset, the proposed method showed a 53.85% reduction in the total number of false positives compared with that of the Gaussian filter while maintaining the total number of true positives. The F-1 score of the proposed method shows a 1.39% improvement compared with those of Lanczos-3 that shows the best performance case.