Multimodal Approach for Face Recognition using 3D-2D Face Feature Fusion

International Journal of Image Processing

Naveen S¹  R.S Moni¹ 

[1] $$

关键词: Point Cloud;    Rotation Invariance;    Pose Correction;    Depth Map;    Spectral Transformations;    Texture Map and Principal Component Analysis.;   

DOI  :  

来源: Computer Science Journals

【 摘 要 】

3D Face recognition has been an area of interest among researchers for the past few decades especially in pattern recognition. The main advantage of 3D Face recognition is the availability of geometrical information of the face structure which is more or less unique for a subject. This paper focuses on the problems of person identification using 3D Face data. Use of unregistered 3D Face data for feature extraction significantly increases the operational speed of the system with huge database enrollment. In this work, unregistered 3D Face data is fed to a classifier in multiple spectral representations of the same data. Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) are used for the spectral representations. The face recognition accuracy obtained when the feature extractors are used individually is evaluated. The use of depth information alone in different spectral representation was not sufficient to increase the recognition rate. So a fusion of texture and depth information of face is proposed. Fusion of the matching scores proves that the recognition accuracy can be improved significantly by fusion of scores of multiple representations. FRAV3D database is used for testing the algorithm.

【 授权许可】

Unknown