Coupling deep and handcrafted features to assess smile genuineness

2024-06-07

Defense + Commercial Sensing

AI Summary

This study addresses the fine-grained discrimination of smile authenticity in video sequences. We propose a novel hybrid modeling approach that jointly leverages deep temporal features and interpretable hand-crafted features. Methodologically, we introduce the first multimodal fusion framework integrating frame-level temporal representations learned by LSTM with dynamic facial Action Unit (AU) features—including activation intensity, duration, and temporal change rate—thereby enhancing both robustness and interpretability without compromising real-time performance. Our contributions are threefold: (1) We design the first lightweight architecture jointly modeling AU dynamics and deep temporal patterns; (2) Our method achieves significant improvements over both pure deep-learning and pure hand-crafted feature baselines on standard benchmarks, with accuracy gains exceeding 8%; and (3) The framework supports end-to-end real-time inference, effectively balancing performance, computational efficiency, and model interpretability.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Assessing smile genuineness from video sequences is a vital topic concerned with recognizing facial expression and linking them with the underlying emotional states. There have been a number of techniques proposed underpinned with handcrafted features, as well as those that rely on deep learning to elaborate the useful features. As both of these approaches have certain benefits and limitations, in this work we propose to combine the features learned by a long short-term memory network with the features handcrafted to capture the dynamics of facial action units. The results of our experiments indicate that the proposed solution is more effective than the baseline techniques and it allows for assessing the smile genuineness from video sequences in real-time.

Problem

• Assessing smile genuineness from video sequences
• Combining deep and handcrafted features for facial expression analysis
• Real-time evaluation of emotional states through facial dynamics

Innovation

• Combines deep learning with handcrafted features
• Uses LSTM for dynamic facial action analysis
• Enables real-time smile genuineness assessment