Random Projections for face detection under resource constraints

Face detection is a key component in numerous computer vision applications. Most face detection algorithms achieve real-time performance by some form of dimensionality reduction of the input data, such as Principal Component Analysis. In this paper, we are exploring the emerging method of Random Projections (RP), a data independent linear projection method, for dimensionality reduction in the context of face detection. The benefits of using random projections include computational efficiency that can be obtained by implementing matrix multiplications with a small number of integer additions or subtractions. The computational savings are of great significance in resource constrained environments, such as wireless video sensor networks. Experimental results suggest that RP can achieve performance that is comparable to that obtained with traditional dimensionality reduction techniques for face detection using support vector machines.