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Face detection exemplifies an essential stage in most of the applications that are interested in visual understanding of human faces. Recently, face detection witnesses a huge improvement in performance as a result of dependence on convolution neural networks. On the other hand, classical face detectors in many renowned open source libraries for computer vision like OpenCV and Dlib may suffer in performance, yet they are still used in many industrial applications. In this paper, we try to boost the performance of these classical detectors and suggest a fusion method to combine the face detectors in OpenCV and Dlib libraries. The OpenCV face detector using the frontal and profile models as well as the Dlib HOG-based face detector are run in parallel on the image of interest, followed by a skin detector that is used to detect skin regions on the detected faces. To figure out the aggregation method for these detectors in an optimal way, we employ a shallow neural network. Our approach is implemented and tested on the popular FDDB and WIDER face datasets, and it shows an improvement in the performance compared to the classical open source face detectors.

Optimal Score Fusion via a Shallow Neural Network to Improve the Performance of Classical Open Source Face Detectors