Human Facial Emotion Recognition using Adaptive Sigmoidal Transfer Function in MLP Neural Network
Ismath Unnisa1, Loganathan. R2
1Mrs. Ismath Unnisa*, Department of Computer Science & Engineering, H.K.B.K College of Engineering, Bangalore, India.
2Dr. Loganathan. R, Department of Computer Science & Engineering, H.K.B.K College of Engineering, Bangalore, India.
Manuscript received on September 13, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 4103-4113 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1347109119/2019©BEIESP | DOI: 10.35940/ijeat.A1347.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The human face is very sensitive towards inner feelings particularly with different state of mind under various conditions. The facial expression has used in computer vision to understand the human response against stimuli. But the facial expression is also having the nature of variability and controllability hence its complete generalization from a computer vision point of view is very difficult and challenging, though acceptable performances can be achieved. In this paper, a two stage based facial expression recognition model which carry the Principal component analysis as a feature extractor in the first stage and self-adaptive based activation function in feedforward neural network as a classifier in the second stage have applied. Use of principal component analysis reduces the dimension of features while the adaptive slope of transfer function provides another parameter along with weights to change in making learning faster and accurate. Six most dominant state of facial emotion like angry, surprise, sadness, normal, happy and fear have considered in this paper and performances have been tested over variable expressions. The benefit of the proposed model of self-adaptive activation function has verified through the benchmark XOR problem classification.
Keywords: Emotion, Facial emotion recognition, principal component analysis, neural network, MLP, Adaptive transfer function.