Real time facial expression and gender recognition with feature integrated CNN

For social communication, facial expression recognition is crucial; further improvement with additional facial attributes like gender can boost the performance of intelligent applications. Recently, the incorporation of deep learning methods extended the research of facial expressions and gender recognition. The deep learning method gives a better detection rate but incurs high computation costs and memory requirements. In this regard, a feature-integrated convolution neural network, namely FusedEGNET, has been designed with fewer parameters to deploy in real-world environments. Further, it utilizes joint supervision with softmax and centre loss to learn the discriminative features of facial expressions. Besides, evaluations of five publicly available facial expression datasets, namely FER-2013, JAFFE, CK+, KDEF, and FERPlus, along with the Adience and CK + datasets for gender recognition, have also been carried out. Finally, to examine the efficiency of the proposed method, an application has been developed and applied to detect real-time facial expressions and gender.