Simply put cross-age effect or own-age bias is the phenomenon that when guessing the age of individuals from their faces, it is claimed that the age of the individual who is guessing the facial age effects the guessing process and the effect is towards to the age of the one who is guessing. In our study; the phenomenon is investigated in humans to observe if such effect exists, then it is tested if the error could be reduced via utilizing convolutional neural networks on face images and classify the face images with respect to their ages.

Date: 27.06.2019 16:30 Place: A-212

The study deals with the emotion recognition problem in Turkish single word pronunciations through the image recognition perspective. The CNN approach allowed us to use spectrograms as the training material. The resulting model has a feasible predictive power and shares trends with the human judges: it is robust to the changes in the sound signal at higher frequencies and generally performed better than the judges except for the 500-8000 hertz band where the human judges did not lose any significant predictive power. The model proved to be a feasible explanation of human assessment of emotions by failing at 500-8000 hertz band where the humans remained robust.

Date: 27.06.2019 13:30   Place: A-108