Automatic tooth detection of intraoral radiographs shows progressively importance in massive forensic verification. Since intraoral radiographs acquired from small oral cavity reveal great variation of intensity and distortion of structure morphology, automatic tooth detection poses a huge challenge. Enhancement methods may not effectively augment informative grayscale gradient. We proposed an intensity-diversification method to increase the detection rate of the tooth through different intensity spaces. The diversification process attempted to explore the image information that was expressible by intensity transform function. In this study, gamma transform was employed to generate different intensity-diversified images from the test radiograph. Deformable statistical Active Appearance Models (AAM) was used to detect a possible mandibular molar tooth region on the images. The AAM regions detected from intensity-diversified images were compared and ranked using three methods: histogram-based, edge-based and crown-root approximation- based methods. Since edge-based and crown-root approximation-based methods revealed higher accuracies, the top five matches in the ranking lists from these two methods were consequently voted by the Borda count to get the most suspected tooth region. Totally, 419 images from 367 patients were used in this study, 100 images for training and 319 images for testing. In our results, the correct detection rate was 71%, comparing to only 45% detection rate of the images without intensity-diversification. AAM outlines were detected in all 319 images, but not all of them belonged to valid tooth regions. In original images, 144 images had valid tooth outlines detected by AAM; 175 images were detected with invalid tooth regions. The true positive rate is 45% and the false positive rate 55%. With intensity-diversification and proposed matching methods, 227 AAM outlines detected were valid tooth regions, and 92 outlines were invalid tooth regions. The true positive rate is 71% and false positive 29%. This results supported that intensity-diversification process could improve automatic detection rate of mandibular molar in intraoral radiographs.