Expression Invariant Features for Face Recognition
Menta Mohit1, Neralla Harichandana2, Pendem Bhagyasri3, P. M. Ashok Kumar4

1Dr. P. M. Shok Kumar, Associate Professor, Department of Computer Science and Engineering, KL University, Andhra Pradesh, India.
2N. Harichandana, Student Department of Computer Science and Engineering, (CSE) Koneru Lakshmaiah Education Foundation Vaddeswaram, Guntur District, Andhra Pradesh, India.
3M. Mohit, Department of Computer science and Engineering Koneru Lakshmaiah Education Foundation ,Vaddeswaram, Guntur District. Andhra Pradesh, India.
4P. Bhagya, Department of Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation, Vaddeswaram, Guntur District, Andhra Pradesh, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2012-2016 | Volume-9 Issue-2, December, 2019. | Retrieval Number:  B3099129219/2019©BEIESP | DOI: 10.35940/ijeat.B3099.129219
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Abstract: Personal Computer sourced Face Recognition has been a sophisticated and well-found technique which is being rationally utilized for most of the authenticated cases. In reality, there is a number of situations where the expressions of the face will be different. We are here able to instinctively detect the five universal expressions: smile, sadness, anger, surprise, neutral by studying face geometry by determining which type of facial expression has been carried out. Using some facial data with variant expressions. We hereby made some experimentations to calculate the accuracies of some machine learning methods by making some changes in the face images such as a change in expressions, which at last needed for training and recognition identifiers. Our objective is to take the features of neutral facial expressions and add them with the other expressive face images like smiling, angry, sadness to improve the accuracy.
Keywords: Face recognition, CNN, facial expressions.