Three-dimensional palatal rugae recognition based on cyclic spectral analysis. (July 2021)

• Record Type: Journal Article
• Authors: Zhang, Xiong; Luo, Qiang; Shangguan, Hong; Wu, Youcheng; Li, Bing; Yang, Jie
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: 2D two-dimensional -- 3D three-dimensional -- DNA deoxyribonucleic acid -- FAM fast Fourier transform accumulation method -- FFT fast Fourier transform -- SCF spectral correlation function; Palatal rugae -- Cyclic spectrum -- Isometric slicing -- Image recognition -- Forensic identification; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102718
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Rapid forensic identification is possible with automated palatal rugae recognition. A digital acquisition scheme for three-dimensional palatal rugae data is designed. Experimental results indicate that the accuracy of the proposed method reaches 95 %. Abstract: Objective: Palatal rugae represent a new type of biometric characteristic that has a low cost and cannot be easily destroyed or forged. It can be used in forensic identification in special environments. However, prior research on palatal rugae recognition relied on the participation of forensic experts. In this study, a digital acquisition scheme of three-dimensional palatal rugae data is designed. Methods: According to the characteristics of palatal rugae, a cyclic spectrum is introduced as the recognition feature, and a k-nearest neighbor classifier is used to realize palatal rugae recognition. In addition, two strategies are utilized to reduce the computational complexity and information redundancy caused by the large dimension of three-dimensional data. Accordingly, isometric slicing is proposed to reduce the three-dimensional palatal rugae data dimension, and the cyclic spectral feature block partition scheme is then used to reduce the feature dimension. Results: The experimental results indicate that the accuracy of the proposed method is as high as 95 %. Conclusion: The proposed method uses pattern recognition technology to realize automatic palatal rugae recognition, which can reduce the dependence on forensic experts to achieve rapid forensic identification. Significance: This attempt is helpful in promoting the digitization of a forensic identification system based on palatal rugae.