چکیده مقاله
The exponential growth of video surveillance data in large scale organizations has created new opportunities to assess customer satisfaction beyond traditional surveys and interviews, which often suffer from bias, cost, and scalability limitations This paper introduces CS FER, a hybrid CNN–Transformer framework that translates facial emotion recognition into a quantifiable Customer Satisfaction Index CSI The system leverages convolutional layers for spatial feature extraction and transformer based self attention for temporal modeling, enabling robust real time analysis of customer emotions under varying environmental conditions Empirical evaluation across benchmark datasets FER2013, AffectNet, RAF DB demonstrates that CS FER achieves 92 6% sequence accuracy and 0 91 macro F1, outperforming conventional FER models More importantly, the derived CSI correlates strongly with human rated satisfaction scores r = 0 78, R² = 0 61 , validating its managerial relevance Simulation of CSI driven interventions further suggests tangible organizational benefits, including improved customer retention and enhanced employee responsiveness By bridging the gap between technical precision in emotion recognition and actionable insights for human resource management, CS FER highlights the potential of AI augmented, human centered management Future research will extend this framework through multimodal data integration, edge optimization, and ethical by design practices to ensure scalability, transparency, and trust in real world organizational contexts
کلیدواژهها
نویسندگان
Babak Ghafari
Department of Computer Engineering, Ma C , Islamic Azad University, Mashhad, Iran
Yalda Kheirkhah
Department of Computer Engineering, Ma C , Islamic Azad University, Mashhad, Iran
Zahra Sadritabatabaie
Vocational Instructor, Region ۷, Department of Education, Mashhad, Iran
شیوه ارجاع
Ghafari, Babak and Kheirkhah, Yalda and Sadritabatabaie, Zahra,1404,A CNN–Transformer Framework (CS-FER) for Real-Time Facial Emotion Recognition and Customer Satisfaction Analytics in AI-Augmented Human Resource Management,1th national conference on challenges of human capital management in large scale organizations,Mashhad
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