Convolutional Neural Network Hyperparameters optimization for Facial Emotion Recognition
This paper presents a method of optimizing the hyperparameters of a convolutional neural network in order to increase accuracy in the context of facial emotion recognition. The optimal hyperparameters of the network were determined by generating and training models based on Random Search algorithm applied on a search space defined by discrete values of hyperparameters. The best model resulted was trained and evaluated using FER2013 database, obtaining an accuracy of 72.16%.
Code (2)
Tasks
Emotion RecognitionFacial Emotion RecognitionFacial Expression Recognition (FER)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Facial Emotion Recognition: State of the Art Performance on FER2013
Facial emotion recognition (FER) is significant for human-computer interaction such as clinical practice and behavioral description. Accurate and robust FER by computer models remains challenging due to the heterogeneity…
Computational EfficiencyEmotion RecognitionFacial Emotion RecognitionFacial Expression Recognition (FER)Facial Emotion Recognition using Convolutional Neural Networks
Facial expression recognition is a topic of great interest in most fields from artificial intelligence and gaming to marketing and healthcare. The goal of this paper is to classify images of human faces into one of seven…
Emotion RecognitionFacial Emotion RecognitionFacial Expression RecognitionFacial Expression Recognition (FER)+1Micro-Facial Expression Recognition in Video Based on Optimal Convolutional Neural Network (MFEOCNN) Algorithm
Facial expression is a standout amongst the most imperative features of human emotion recognition. For demonstrating the emotional states facial expressions are utilized by the people. In any case, recognition of facial …
Emotion RecognitionFacial Expression RecognitionFacial Expression Recognition (FER)MarketingLeveraging Previous Facial Action Units Knowledge for Emotion Recognition on Faces
People naturally understand emotions, thus permitting a machine to do the same could open new paths for human-computer interaction. Facial expressions can be very useful for emotion recognition techniques, as these are t…
Emotion RecognitionI Know How You Feel: Emotion Recognition with Facial Landmarks
Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life. Currently propose…
ClassificationEmotion ClassificationEmotion RecognitionGeneral Classification+1