Real time human emotion recognition using artificial neural networks - Muni Reddy, T.; S.Venkatanarayanan, Dr. (International Journal of Engineering and Technology, 2019)
Now a day’s one of the unsolved problem in computer vision is recognizing or understanding other people's emotions and feelings. Deep Convolutional Neural Networks (CNN) has tried to be economical in feeling recognition issues. The good degree of performance achieved by these classifiers can be attributed to their ability to self-learn a down-sampled feature vector that retains abstraction info through filter kernels in convolutional layers. In this paper we have a tendency to explore the impact of coaching the initial weights in associate unsupervised manner. we have a tendency to study the result of pre-training a Deep CNN as a Convolutional Auto-Multiplexer (CAM) in a very greedy layer-wise unsupervised fashion for emotion recognition mistreatment facial features pictures. once trained with at random initialized weights, our CNN feeling recognition model achieves a performance rate of 92.16% on the Karolinska Directed Emotional Faces (KDEF) dataset. In distinction, by using thispre-trained, the performance will increase to 93.52%. Pre-training our CNN as a CAM conjointly reduces coaching time marginally