AdaBoost Face Detection Based on Few Training Samples
Juan Zhang · Jisuanji gongcheng · 2011
Aiming at the complex and long training process of face detection,this paper addresses the problem of learning to detect faces from a small set of training samples.It proposes to use covariance features to extract the facial features.For better classification performance,linear hyperplane classifier based on Fisher Discriminant Analysis(FDA) is proffered.AdaBoost algorithm is used to construct cascade classifier.It shows that the detection can be significantly improved with the algorithm on a small dataset,compared with Haar-like features used in current most face detection systems.