Facial Emotion Recognition by Hyper-Parameter tuning of Convolutional Neural Network using Genetic Algorithm

Sammanfattning: Context: Importance of facial emotion recognition is increasing significantly as it's applications play a key role in several sectors and fields. Deep learning techniques in machine learning provide good performance in facial recognition tasks, Where as deep neural networks like convolutional neural networks are most widely used for image recognition and classification tasks. However, these neural networks depend on configuration parameters called hyper-parameters. So, tuning these parameters play a vital role in facial emotion recognition. Moreover, it is challenging and time consuming to tune the hyper-parameters of neural networks since it involves many parameters. Tuning these hyper-parameters is considered as optimization task where evolutionary algorithms like genetic algorithms play a major role. Studying and experimenting different genetic algorithm concepts not only provide interesting insights for facial recognition tasks but also provide significant progresses in deep learning, gaming, and virtual reality. Objectives: The thesis aims to develop a model for facial emotion recognition by applying evolutionary mechanisms like genetic algorithms on convolutional neural networks. The developed model recognizes seven basic emotions in images of human beings such as fear,happy, surprise, sad, neutral, disgust and angry using FER-2013(facial emotion recognition) dataset. Methods: Emotion recognition of the facial images is done by hyper-tuning of convolutional neural network using evolutionary mechanisms. Literature review is performed for studying the working mechanism of genetic algorithm, techniques, best methods of genetic algorithms, genetic operators for hyper-parameter tuning of neural network. After studying the methods, experiment is conducted to evaluate and study the impact of applying genetic algorithm methods in hyper-parameter tuning which in turn helps in facial emotion recognition. Results: Genetic algorithm concepts which are identified from literature review improved the performance of convolutional neural network. Elitism and multiparent recombination concepts of genetic algorithm showed satisfying results by significantly boosting the performance of neural networks. Multipoint cross-over established a new theme in genetic algorithm by introducing sharp variations and gave scope for genetic diversity which results in increasing efficiency of neural network. Performed experimental model portrayed these concepts and has improved the performance of convolutional neural network. Conclusions: The genetic algorithm worked constructively for the improvement of performance of convolutional neural networks. Results from experimental model portrayed improvement of neural network and has helped in increasing accuracy of the images of facial emotion recognition. Variable length genetic algorithm helped the model in tracing out important variable parameters thus helping the neural networks to perform better. Different genetic mechanisms have different functions for effective functioning of neural network. Key observations, new insights gained from the experimental results of the current research are helpful and expand the scope of deep learning applications with evolutionary mechanisms.