Integrated Face Analytics Networks through Cross-Dataset Hybrid Training
In this paper a two-step face detection technique is proposed. The first step uses a conventional skin detection method to extract regions of potential faces from the image database. This skin detection step is based on a Gaussian mixture model in the YCbCr colour space. In the second step faces are detected among the candidate regions by filtering out false positives from the skin colour detection module. The selection process is achieved by applying a learning approach using multiple additional features and a suitable metric in multi-feature space. The metric is derived by learning the underlying parameters using a small set of representative face samples. In this process the parameters are optimized by a Multiple Objective Optimization method based on the Pareto Archived Evolution Strategy. The learned metric in multi-feature space is then applied to a conventional classifier to filter out non faces from the first processing step. Support Vector Machines and KNearest Neighbours classifiers are used to test the performance of the optimized metric in multi-feature space.