Facial Expression conveys non-verbal communication, which plays an important role in acquaintance among people. The Facial Expression Detection system is an activity to identify the emotional state of a person. In this system, a captured frame is compared with trained dataset that is available in database and then state of the captured frame will be defined. This system is based on Image Processing and Machine Learning. For designing a robust facial feature descriptor, we apply the Xception Modelling Algorithm. Xception is a convolutional neural network that is trained on more than a million images from the ImageNet database. The network is 71 layers deep and can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the network has learned rich feature representations for a wide range of images. The detection performance of the proposed method will be evaluated by loading the data-set and pre-processing the images for feeding it to CNN model. Experimental results with prototypic expressions show the superiority of the Xception-Model descriptor against some well- known appearance-based feature representation methods. Experimental results demonstrate the competitive classification accuracy of our proposed method.
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[v1] 2019-11-22 16:41:31
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