Abstract
Facial expression recognition (FER) systems have attracted much research interest in the area of Machine Learning. We designed a large, deep convolutional neural network to classify 40,000 images in the dataset into one of seven categories (disgust, fear, happy, angry, sad, neutral, surprise). In this project, we have designed deep learning Convolution Neural Network (CNN) for facial expression recognition and developed model in Theano and Caffe for training process. The proposed architecture achieves 61% accuracy. This work presents results of accelerated implementation of the CNN with graphic processing units (GPUs). Optimizing Deep CNN is to reduce training time for system.
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Chavan, U., Kulkarni, D. (2019). Optimizing Deep Convolutional Neural Network for Facial Expression Recognitions. In: Balas, V., Sharma, N., Chakrabarti, A. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-13-1402-5_14
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DOI: https://doi.org/10.1007/978-981-13-1402-5_14
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