Authors:
Sujata
and
Suman K. Mitra
Affiliation:
Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, Gujarat, India
Keyword(s):
CNN, DNN, VGG16, HOG, HSOG, SVM, KNN.
Abstract:
Recognizing human expressions is an important task for machines to understand emotional changes in humans. However, the the accurate features that are closely linked to changes in expression are difficult to extract due to the influence of individual differences and variations in emotional intensity. The modular approach presented here imitates the human being’s ability to identify a person with a limited facial part. In this article, we demonstrate experimentally that certain parts of the face, such as the eyes, nose, lips and forehead, contribute more to the recognition of expressions. A combination of two deep neural networks is also proposed to extract the characteristics of the facial images provided. Two preprocessing approaches are implemented, Histogram Equalization (to handle illumination) and Data Augmentation (increasing number of facial images), to restrict the regions used for recognition of the facial expression. Two-channel architecture used for implementation, one cha
nnel accepts input as a grayscale face image, processed by VGG16_ft (fine-tuned VGG16), and another channel accepts input as histograms face image. the second order gradients (HSOG), processed from the proposed CNN model and extracts the characteristics accordingly. Then concatenate the characteristics from the two channels. The final recognition result is calculated using the SVM and KNN classifiers. Experimental results indicate that the proposed algorithm is able to recognize six basic facial expressions (happiness, sadness, anger, disgust, fear and surprise) with great precision. Fine tuning is effective for FER activities with a well-trained model if there are not enough samples to collected.
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