Complexity Reduced Face Detection Using Probability-Based Face Mask Prefiltering and Pixel-Based Hierarchical-Feature Adaboosting

Efficient face detection method with eye region judgment

“…However, the practical training time is approximately 1,500 times that of the PBH features. The reason is that the large number of Haar-like features causes data swapping between the memory and the hard drive, whereas that of the proposed PBH features can be completely stored in the memory, which results in the large difference in the training time [15]. Because the number of features in the proposed system only added four crucial JEER features to the PBH features, its training time is slightly longer than that of the hybrid Adaboost-based face detection system but is significantly shorter than that of the traditional Adaboost face detection system.…”

“…Thereafter, the integral image uses seven operations to add the feature values for the weak classifier when the weak classifier is a two-rectangle feature, which entails the least number of operations. Therefore, the best scenario for the integral image to sum up the feature values of the K weak classifiers is 7 × K [15]. The lower part of Table 2 shows that the best scenario is 3 × N × N + 7 × K = 3 × 24 × 24 + 7 × 17 = 1,847 when all 17 weak classifiers are two-rectangle features.…”

“…The computational complexity of the hybrid Adaboostbased face detection system is much lesser than that of the traditional Adaboost face detection system. PFMPF, which can filter 85% of the nonface images, uses N × N operations in the detection window of size N × N, and the PBH features use less than 2 × N × N operations for the weak classifiers because the sum of the PBH features in a detection window of size N × N is not greater than N × N [15]. Therefore, 85% of the images use N × N = 24 × 24 = 576 operations, and 15% of the images use less than (N × N) + 2 × (N × N) = (24 × 24) + 2 × (24 × 24) = 1,728 operations.…”

“…This section presents the proposed face detection system that applies JEER to the hybrid Adaboost-based face detection system of PFMPF and PBHFA, which was proposed by Guo et al, to reduce the training time of the traditional system [15]. PFMPF does not use the training features as classifiers but employ a probability-based face mask to exclude the nonface.…”

“…Lienhart et al presented the rotated Haar-like features to enhance the detection performance of rotated faces [14]. Guo et al presented a two-stage hybrid face detection system composed of the probability-based face mask pre-filtering (PFMPF) and the pixel-based hierarchical feature Adaboosting (PBHFA) to reduce the training time [15]. First, PFMPF does not use the training features as classifiers but employ a probability-based face mask to exclude the nonface.…”