A Multi-Task Learning Framework for Character Face Recognition and Emotion Analysis in Television Programs
Xingyu Chen, Zhen Wang, Gang Wang · Traitement du signal · 2025
With the rapid expansion of content in the digital and intelligent era, there is an increasing demand for fine-grained character analysis in television programs.As core technologies of artificial intelligence, face recognition and emotion analysis face significant challenges in complex media scenarios, including variable lighting conditions, diverse facial poses, and dynamic expressions.Traditional single-task models often struggle to process such multidimensional information efficiently.Existing studies indicate that conventional face recognition methods typically rely on single-task learning, overlooking intrinsic correlations with tasks like emotion analysis, which results in poor generalization in complex environments.Likewise, emotion analysis often suffers from underutilized features and insufficient exploitation of shared information between tasks.Moreover, these two tasks are frequently treated independently, lacking an integrated analytical framework.To address these issues, this paper proposes a unified character analysis framework based on multi-task learning for television programs.The framework comprises two key components: (1) the construction of a multi-task learning model that jointly learns face recognition along with auxiliary tasks such as facial landmark detection and expression classification, thereby enhancing feature sharing and representation capabilities in complex settings, and improving the accuracy and robustness of face recognition; and (2) the design of an emotion analysis module built upon face recognition results, which integrates multi-dimensional features such as facial expressions, head pose, and eye movements.This module leverages multi-task or deep learning techniques to achieve real-time and accurate emotion recognition.By incorporating multi-task learning, the proposed framework effectively addresses the limitations of traditional approaches, such as task isolation and inefficient feature utilization.It provides a unified solution that integrates face recognition and emotion analysis, offering significant theoretical and practical value in areas such as media production optimization, enhanced user experience, and intelligent content recommendation.