Endüstri Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1942
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Article Citation - WoS: 1Facial Emotion Recognition Using Residual Neural Networks(Aves, 2024-11-08) Kırbız, SerapFacial emotion recognition (FER) has been an emerging research topic in recent years. Recent automatic FER systems generally apply deep learning methods and focus on two important issues: lack of sufficient labeled training data and variations in images such as illumination, pose, or expression-related variations among different cultures. Although Convolutional Neural Networks (CNNs) are widely used in automatic FER, they cannot be used when the number of layers is large. Therefore, a residual technique is applied to CNNs and this architecture is named residual neural network. In this paper, an automatic facial emotion recognition method using residual networks with random data augmentation is proposed on a merged FER dataset consisting of 41,598 facial images of size 48 × 48 pixels from seven basic emotion classes. Experimental results show that ResNet34 with data augmentation performs better than CNN with a classification accuracy of 81%.Book Part Citation - Scopus: 3Selection of the Best Face Recognition System for Check in and Boarding Services(Springer, 2021-08-27) Ucal Sarı, İrem; Sergi, Duygu; Kuchta, DorotaCheck-in and boarding services are one of the most human oriented pre-flight services in aviation industry. The process of using face recognition systems increase with the aviation 4.0 concept, decreases need for manpower and increases the efficiency of the processes. Therefore, problems, developments and challenges of face recognition in terms of aviation 4.0 are discussed in this chapter to determine the best face recognition system for check in and boarding systems. Analytic hierarchy process and grey relational analysis are used to analyze current system providers. To handle the ambiguity in the linguistic evaluations, fuzzy Z- numbers are used. 10 face recognition system providers are evaluated according to five criteria with the proposed methodology and the results are discussed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.