Performance Analysis of State-of-the-Art Deep Learning Models in the Visual-Based Apparent Personality Detection

This paper analyses the performances of pre-trained deep learning models as feature extractors forapparent personality trait detection (APD) by utilising different statistical methods to find the bestperforming pre-trained model. Accuracy and computational cost were used to measure the modelperformance. Personality is measured using the Big Five Personality Schema. CNN-RNN networks weredesigned using VGG19, ResNet152, and VGGFace pre-trained models to measure the personality withscene data. The models were compared using the mean accuracy attained and the average time is taken fortraining and testing. Descriptive statistics, graphs, and inferential statistics were applied in modelcomparisons. Results convey that, ResNet152 model reported the highest mean accuracy in the test dataset(0.9077), followed by VGG19 with 0.9036; VGGFace recorded the lowest (0.8962). ResNet152 consumedmore time than other architectures in model training and testing since the number of parameters iscomparably higher than the other two architectures involved. Statistical test results prove no significantevidence to conclude that VGG19 and ResNet152 based CNN-RNN models performed differently. Thisleads to the conclusion that even with a comparably lower number of parameters VGG19 model performedwell. The findings reveal that satisfactory accuracy is obtained with a limited number of frames extractedfrom videos since models achieved more than 90% accuracy.