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Sparse Coding of Shape Trajectories for Facial Expression and Action Recognition.

Amor Ben Tanfous, Hassen Drira, Boulbaba Ben Amor

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Summary

This study uses Sparse Coding and Dictionary Learning to analyze human shape trajectories in videos. The method effectively recognizes actions and expressions by addressing nonlinearities in shape data.

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Area of Science:

Computer Vision
Machine Learning
Human Behavior Analysis

Background:

Human landmark detection and tracking in videos are crucial for automatic behavior understanding.
Time-varying geometric data from RGB-D sensors present challenges due to nonlinear shape manifolds.
Conventional machine learning struggles with the inherent nonlinearity of shape trajectories.

Purpose of the Study:

To develop a robust method for studying time-varying shapes and their temporal evolution (trajectories).
To apply Sparse Coding and Dictionary Learning to Kendall shape spaces for analyzing 2D and 3D landmark data.
To enhance action and expression recognition using shape trajectory analysis.

Main Methods:

Utilized Sparse Coding and Dictionary Learning on Kendall shape spaces for 2D and 3D landmark data.
Explored intrinsic and extrinsic solutions to overcome the nonlinearity of shape spaces.
Applied the approach to 3D skeletal sequences for action recognition and 2D facial landmarks for expression recognition.

Main Results:

Shape trajectories provide more discriminative time-series data.
The proposed method yields computational properties like sparsity and vector space structure.
Demonstrated competitiveness against state-of-the-art methods on standard datasets.

Conclusions:

Sparse Coding and Dictionary Learning effectively model nonlinear shape trajectories for human behavior analysis.
The approach enhances the discriminative power of time-series data for action and expression recognition.
The method offers a computationally efficient and effective solution for analyzing complex geometric data in videos.