Face detection using data and sensor fusion techniques

Face detection is an important part of any face recognition system and it is a difficult task to detect faces of arbitrary size, orientation and location. Fusion is a technique which combines the results from two or more distinct methods in such a way that better results are obtained. We propose two different fusion techniques, data fusion and sensor fusion, that combine statistical and shape based methods. Four different features, eigenweights from Principal Component Analysis (PCA), Legendre Moments (LM), Zernike Moments (ZM) and Generic Fourier Descriptor (GFD), are used for our fusion methods. In data fusion technique, the calculated eigenweights, moments, and descriptors are concatenated together to form a feature vector. This combined feature is then classified as face or non-face pattern using Support Vector Machine (SVM). In the case of sensor fusion technique, each of the feature vectors obtained by individual methods are first classified using individual SVMs. Then the class label obtained from the individual SVMs are concatenated together to form a feature vector. This feature vector is then classified using another SVM. Both the techniques produced better results compared with the individual methods. For a database containing 300 face images and 200 non-face images, both of our methods achieved 0% false rejection rate. 99.75% accuracy was achieved for data fusion method, while sensor fusion method produced 99.9% accuracy.