Fast gender recognition in videos using a novel descriptor based on the gradient magnitudes of facial landmarks
George Azzopardi, Antonio Greco, Alessia Saggese, Mario Vento · 2017
The growing interest in recent years for gender recognition from face images is mainly attributable to the wide range of possible applications that can be used for commercial and marketing purposes. It is desirable that such algorithms process high resolution video frames acquired by using surveillance cameras in real-time. To the best of our knowledge, however, there are no studies which analyze the computational impact of the methods and the difficulties related to the processing of faces extracted from videos captured in the wild. We propose a novel face descriptor based on the gradient magnitudes of facial landmarks, which are points automatically extracted from the face contour, eyes, eyebrows, nose, mouth and chin. We evaluate the effectiveness and efficiency of the proposed approach on two new datasets, which we made available online and that consist of color face images and color video sequences acquired in real scenarios. The proposed approach is more efficient and effective than three commercial libraries.