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3D face recognition based on multiple keypoint descriptors and sparse representation.

Lin Zhang¹, Zhixuan Ding¹, Hongyu Li¹

¹School of Software Engineering, Tongji University, Shanghai, China.

|June 19, 2014

Summary

This summary is machine-generated.

This study introduces a new 3D face recognition method using multiple keypoint descriptors (MKD) and sparse representation-based classification (SRC). The 3DMKDSRC approach effectively handles missing data, expressions, and occlusions for robust 3D face identification.

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Area of Science:

Computer Vision
Biometrics
Pattern Recognition

Background:

3D face recognition is gaining interest.
Existing 3D scans face challenges like missing data, expressions, and occlusions.
Robust 3D face recognition is crucial for real-world applications.

Purpose of the Study:

To propose a novel general approach for 3D face recognition.
To address challenges of missing data, expressions, and occlusions in 3D face scans.
To develop a method that does not require pre-alignment of face scans.

Main Methods:

The proposed method, 3DMKDSRC, utilizes multiple keypoint descriptors (MKD) and sparse representation-based classification (SRC).
3D face scans are represented by descriptor vectors extracted from keypoints using meshSIFT.
Identity is determined using multitask SRC on probe scan descriptors against a gallery dictionary.

Main Results:

The 3DMKDSRC approach demonstrates robustness to missing data, occlusions, and expressions.
Extensive experiments on Bosphorus, GavabDB, and FRGC2.0 databases confirm its superiority over leading schemes.
The method does not require pre-alignment between 3D face scans.

Conclusions:

3DMKDSRC offers a robust and effective solution for 3D face recognition.
The method's ability to handle common real-world challenges makes it suitable for practical applications.
Publicly available source code and results facilitate further research and development.