# Facial Emotion Recognition Analysis Based on Age-Biased Data
###### Abstract  
This paper aims to analyze the importance of age-biased data in recognizing six emotions using facial expressions. For this purpose, a custom dataset (adults, kids, mixed) was constructed using images that separated the existing datasets (FER2013 and MMA FACILE EXPRESSION) into adults (\(\geq\)14) and kids (\(\leq\)13). The convolutional Neural Networks (CNN) algorithm was used to calculate emotion recognition accuracy. Additionally, this study investigated the effect of the characteristics of CNN architecture on emotion recognition accuracy. Based on the variables of Accuracy and FLOP, three types of CNN architectures (MobileNet-V2, SE-ResNeXt50 (32 \(\times\) 4 d), and ResNeXt-101 (64 \(\times\) 4 d)) were adopted. As for the experimental result, SE-ResNeXt50 (32 \(\times\) 4 d) showed the highest accuracy at 79.42%, and the model that learned by age obtained 22.24% higher accuracy than the model that did not learn by age. In the results, the difference in expression between adults and kids was greatest for fear and neutral emotions. This study presented valuable results on age-biased learning data and algorithm type effect on emotion recognition accuracy. facial emotion recognition; deep learning; convolution neural networks; age bias +
Footnote †: journal  
* [PERSON] et al. conducted an emotion classification study based on a dataset that separated adults and the elderly from the age of 55. The results of the study showed that compared to the elderly, adults had higher accuracy in identifying emotions such as anger, sadness, fear, surprise, and happiness. In this result, it is said that aging and the classification of emotions are related and can affect emotional identification [9]. * [PERSON] et al. conducted a study on emotion classification by constructing datasets of adults (19-31), middle-aged (39-55), and elderly (69-80) participants. The accuracy, using Amazon Recognition, was 89% for adults, 85% for middle-aged, and 68% for the elderly. The emotion classification accuracy between adults and middle-aged people showed a difference of less than 4%, but it was confirmed that the accuracy of emotion classification for the elderly was about 17% lower than that of adults and middle-aged people [10]. * Sullivan et al. studied the classification of emotions of the elderly, and they determined that it was difficult to classify emotions of the elderly when they were consistently expressing anger, fear, and sadness. Therefore, differences in emotional expression methods were studied in the elderly and adults [11]. * A study by [PERSON] et al. found that adults expressed fear and anger more accurately than kids. Adults confirmed higher accuracy in the classification of fear and anger emotions than kids [12]. * [PERSON] et al. studied real-time emotion recognition using CNN MobileNetv2. In this study, the background of the image was learned so that it did not affect the emotional classification. CK+, JAFFE, and FER2013 were used as learning data. The accuracy of emotional classification was CK+ 86.86%, JAFFE 87.8%, and FER2013 66.28% [13]. * A study by [PERSON] et al. used pre-processing steps and several CNN topologies to improve accuracy and training time. MMA FACILE EXPRESSION was used as the learning data, and 35,000 images were used. The accuracy of emotion classification was 69.2% [14]. * [PERSON] et al. used a face-sensitive convolutional neural network (FS-CNN) for human emotion recognition. The proposed FS-CNN is used to detect faces in high-scale images and analyze landmarks on faces to predict facial expressions for emotion recognition. The proposed FS-CNN was trained and evaluated on the UMD Faces dataset. The experiment resulted in an emotion classification accuracy of 94% [15]. In related studies, although differences according to race and gender bias are receiving much attention, age-related biases are not considered.