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Combined AdaBoost and gradientfaces for face detection under illumination problems. IEEE."},{"volume-title":"Face detection in video using combined data-mining and histogram based skin-color model","author":"Tsishkou Dzmitry","key":"e_1_3_2_1_69_1","unstructured":"Dzmitry Tsishkou , Mohamed Hammami , and Liming Chen . 2003. Face detection in video using combined data-mining and histogram based skin-color model . IEEE. Dzmitry Tsishkou, Mohamed Hammami, and Liming Chen. 2003. Face detection in video using combined data-mining and histogram based skin-color model. IEEE."},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-018-01617-y"},{"volume-title":"Rapid object detection using a boosted cascade of simple features","author":"Viola Paul","key":"e_1_3_2_1_71_1","unstructured":"Paul Viola and Michael Jones . 2001. Rapid object detection using a boosted cascade of simple features . IEEE. Paul Viola and Michael Jones. 2001. 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