We propose two novel local transform features: local gradient patterns (LGP) and binary histograms of oriented gradients (BHOG). LGP assigns one if the neighboring gradient of a given pixel is greater than its average of eight neighboring gradients and zero otherwise, which makes the local intensity variations along the edge components robust. BHOG assigns one if the histogram bin has a higher value than the average value of the total histogram bins, and zero otherwise, which makes the computation time fast due to no further postprocessing and SVM classification. We also propose a hybrid feature that combines several local transform features by means of the AdaBoost method, where the best feature having the lowest classification error is sequentially selected until we obtain the required classification performance. This hybridization makes face and human detection robust to global illumination changes by LBP, local intensity changes by LGP, and local pose changes by BHOG, which considerably improves detection performance. We apply the proposed features to face detection using the MIT+CMU and FDDB databases and human detection using the INRIA and Caltech databases. Our experimental results indicate that the proposed LGP and BHOG feature attain accurate detection performance and fast computation time, respectively, and the hybrid feature improves face and human detection performance considerably.