Weakly trained dual features extraction based detector for frontal face detection
Wael Louis, Konstantinos N. Plataniotis · 2010
This paper investigates the inconvenience of using huge number of features, enormous training dataset and lengthy training session to achieve a good performance frontal face detector. The proposed face detector is based on a novel idea which proposes using joint decision from two parallel different features trained detectors, one detector is trained with Local Binary Patterns (LBP) features and the other with Haar-like features. Both detectors are trained with few features using not a huge face/non-face dataset and within relatively short period of time. Hence, both detectors agree on the face image but seldom agree on the non-face image. The result is significantly improved using a multi-detections merging algorithm using simple clustering method. The robustness of the detector is examined once using a face/non-face dataset and compared to Lienhart frontal face detector, and secondly using a real-life sequence.