Expand Training Set for Face Detection by GA Re-sampling

Data collection for both training and testing a classifier is a tedious but essential step towards face detection and recognition. All of the statistical methods suffer from this problem. This paper presents a genetic algorithm (GA)-based method to swell face database through re-sampling from existing faces. The basic idea is that a face is composed of a limited components set, and the GA can simulate the procedure of heredity. This simulation can also cover the variations of faces in different lighting conditions, poses, accessories, and quality conditions. All the collected face samples are aligned and randomly divided into three sub-sets: training, validating, and testing set. The training set is then used to train a Sparse Network of Winnow (SNoW). In addition, it is also used as the initial population of the GA. After each generation, we will use the initial generation and the solutions with high fitness values to re-train the SNoW, and the newly-trained SNoW is used to evaluate ...

• Type: Conference
• Year: 2004
• Where: FGR
• Authors: Jie Chen, Xilin Chen, Wen Gao
• Keywords: Biometrics, Data Collection, Face Detection, FGR 2004, Test Set