Pengenalan Ekspresi Wajah Menggunakan Wavelet Transform Dan Convolutional Neural Network

Machine learning telah menjadi bagian dari kehidupan sehari-hari bagi banyak orang. Salah satu pengaplikasian machine learning adalah pengenalan ekspresi wajah manusia. Pengenalan ekspresi wajah manusia mengkategorikan gambar ekspresi wajah menjadi satu dari banyak kelas ekspresi wajah berdasarkan fitur gambar tersebut. Banyak perusahaan, badan riset dan universitas yang terus mengembangkan machine learning agar mendapat hasil yang lebih akurat dan cepat. Dari situlah lahir algoritma deep learning, yang merupakan bagian dari machine learning. Convolutional Neural Network (CNN) adalah salah satu deep neural network yang cocok digunakan untuk mengolah data yang berbentuk 2 dimensi, seperti gambar dan video. Pada tugas akhir ini, penulis mengusulkan sebuah algoritma untuk mengubah data gambar menjadi Wavelet Domain dengan menggunakan Wavelet Transform. Tujuannya adalah untuk meningkatkan akurasi pengenalan ekspresi wajah manusia dengan metode Convolutional Neural Network. Data pelatihan dan uji coba diambil dari dataset “Karolinska Directed Emotional Faces” (KDEF) yang berisi foto wajah manusia dengan 7 ekspresi berbeda yang nantinya menjadi tujuan pengenalan ekspresi wajah manusia yang dibuat. Praproses terhadap data antara lain dilakukan perubahan format gambar menjadi grayscale, perubahan resolusi gambar menjadi 256x256 piksel, dilakukan proses Discrete Wavelet Transform level 1, dan dilakukan proses augmentasi data berupa refleksi horizontal dan perbesaran ukuran gambar. Hasil uji coba terakhir didapatkan nilai akurasi 89,6%. ================================================================================================ Machine learning has become a part of the daily life of people around the world. One of the application of machine learning is human facial expression recognition. Human facial expression recognition categorizes an image of facial expression into one of many facial expression classes based on the features extracted from the image. Many companies, researchers and universities keep improving the machine learning to get a better and faster result. And from those improvements, deep learning algorithm is born. Convolutional Neural Network (CNN) is one of the deep neural network that suitable to process 2 dimentional data like image and video. In this undergraduate thesis, the images are transformed into Wavelet Domain using Wavelet Transform before being processed into the proposed network. The purpose of this method is to improve the accuracy of the human facial expression recognition using Convolutional Neural Network. The train and test data used in this thesis is taken from “Karolinska Directed Emotional Faces” (KDEF) dataset which contains human facial expression with 7 different expressions which will be the prediction labels of the human facial expression recognition. The preprocessing of the images include changing the image format to grayscale, changing the image resolution to 256x256 pixels, applying level 1 Discrete Wavelet Transform and applying data augmentation with horizontal reflection and zoom in. The final test accuracy is 89,6%

Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities

Time-Efficient Hybrid Approach for Facial Expression Recognition

Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to the best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database