Ozdemir M.A.Elagoz B.Alaybeyoglu Soy A.Akan A.2023-06-162023-06-1620209.78E+12https://doi.org/10.1109/TIPTEKNO50054.2020.9299256https://hdl.handle.net/20.500.14365/36382020 Medical Technologies Congress, TIPTEKNO 2020 -- 19 November 2020 through 20 November 2020 -- 166140In this study, it was aimed to recognize the emotional state from facial images using the deep learning method. In the study, which was approved by the ethics committee, a custom data set was created using videos taken from 20 male and 20 female participants while simulating 7 different facial expressions (happy, sad, surprised, angry, disgusted, scared, and neutral). Firstly, obtained videos were divided into image frames, and then face images were segmented using the Haar library from image frames. The size of the custom data set obtained after the image preprocessing is more than 25 thousand images. The proposed convolutional neural network (CNN) architecture which is mimics of LeNet architecture has been trained with this custom dataset. According to the proposed CNN architecture experiment results, the training loss was found as 0.0115, the training accuracy was found as 99.62%, the validation loss was 0.0109, and the validation accuracy was 99.71%. © 2020 IEEE.trinfo:eu-repo/semantics/closedAccessDeep LearningEmotion RecognitionFacial ExpressionBiomedical engineeringConvolutional neural networksFace recognitionLearning systemsNetwork architectureEmotional stateEthics committeeFacial emotionsFacial ExpressionsFacial imagesImage preprocessingLearning methodsTraining accuracyDeep learningConference Object10.1109/TIPTEKNO50054.2020.92992562-s2.0-85099442002