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Deep Orientation-Guided Gender Recognition from Face Images

Mohamed Selim; Stephan Krauß; Tewodros Amberbir Habtegebrial; Alain Pagani; Didier Stricker
In: Proceedings of the 12th International Conference on Pattern Recognition Systems. International Conference on Pattern Recognition Systems (ICPRS-22), June 7-10, Saint-Étienne, France, IEEE, 2022.


In the recent decade, gender recognition and face analysis has been one of the most researched issues in computer vision. Although several solutions have been provided to the problem of gender recognition from face images, nonetheless, it is regarded as a difficult issue. Deep learning has been proven to solve challenging problems. On the other hand, several existing works have proven their ability to accurately predict the head orientation angles. The remaining error in gender prediction models requires novel solutions to try to improve it further. In this work, we present a novel deep learning-based method to predict gender using both the face image and the head orientation angles. We show that the use of head orientation information consistently boosts the accuracy of gender prediction models. We achieve this by increasing the representational power of deep neural networks by introducing a head orientation adapter. It takes the head angles as input and outputs a vector that is used to recalibrate the deep learning neural networks. The proposed method was tested on a large-scale dataset called AutoPOSE, which has sub-millimeter-accurate head orientation angles. We show that using the head orientation adapter consistently boosts the gender prediction models’ accuracy, and reduces the error by 20%.