Authors:
Sujata
and
Suman K. Mitra
Affiliation:
Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, Gujarat, India
Keyword(s):
CNN, DNN, VGG16, SVM, KNN.
Abstract:
The modular approach mimics the capability of the human brain to identify a person with a limited facial part. In this article, we experimentally show that some facial parts like eyes, nose, lips, and forehead contribute more in the expression recognition task. Deep neural network, VGG16 ft, is proposed to automatically extricate features from the given facial images. Fine-tuning is very fruitful to the FER (Facial Expression Recognition) with pre-trained models, if sufficient facial images are not collected. Two preprocessing approaches, Fourier transform followed by Gabor filters and Data Augmentation (DA), are implemented to restrain the regions used for Facial expression recognition (FER). The features from four facial regions are concatenated and classification is done using SVM and KNN (with different distance measure). The experimental result shows that the proposed framework can recognize the facial expressions like happy, anger, sad, surprise, disgust and fear with high accu
racy for the benchmark datasets like “JAFFE”, “VIDEO”, “CK+” and “Oulu-Casia”.
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