Scale-invariant 3D face recognition using computer-generated holograms and the Mellin transform
利用计算机生成的全息图和梅林变换实现尺度不变的三维人脸识别
コンピュータ生成ホログラムとメリン変換を用いたスケール不変3D顔面認識
컴퓨터 생성 홀로그램과 멜린 변환을 이용한 스케일 불변 3D 얼굴 인식
Reconocimiento facial 3D invariante a escala utilizando hologramas generados por computadora y la transformada de Mellin
Reconnaissance 3D faciale invariante à l'échelle utilisant des hologrammes générés par ordinateur et la transformée de Mellin
Масштабно-инвариантное распознавание 3D-лиц с использованием компьютерно-сгенерированных голографий и преобразования Меллина
Yongwei Yao ¹, Yaping Zhang ¹ ², Huanrong He ¹, Xianfeng David Gu ³, Daping Chu ⁴, Ting-Chung Poon ⁵
¹ Yunnan Provincial Key Laboratory of Modern Information Optics (LMIO), Kunming University of Science and Technology, Kunming 650500, China
中国 昆明 昆明理工大学 云南省现代信息光学重点实验室
² Cambridge Digital Humanities (CDH), University of Cambridge, Cambridge CB2 1RX, UK
³ Computer Science Department, SUNY at Stony Brook, Stony Brook, New York 11794, USA
⁴ Centre for Photonic Devices and Sensors, University of Cambridge, 9 JJ Thomson Avenue, Cambridge CB3 0FA, UK
⁵ Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24061, USA
We present a novel method for scale-invariant 3D face recognition by integrating computer-generated holography with the Mellin transform. This approach leverages the scale-invariance property of the Mellin transform to address challenges related to variations in 3D facial sizes during recognition.
By applying the Mellin transform to computer-generated holograms and performing correlation between them, which, to the best of our knowledge, is being done for the first time, we have developed a robust recognition framework capable of managing significant scale variations without compromising recognition accuracy. Digital holograms of 3D faces are generated from a face database, and the Mellin transform is employed to enable robust recognition across scale factors ranging from 0.4 to 2.0. Within this range, the method achieves 100% recognition accuracy, as confirmed by both simulation-based and hybrid optical/digital experimental validations.
Numerical calculations demonstrate that our method significantly enhances the accuracy and reliability of 3D face recognition, as evidenced by the sharp correlation peaks and higher peak-to-noise ratio (PNR) values than that of using conventional holograms without the Mellin transform. Additionally, the hybrid optical/digital joint transform correlation hardware further validates the method's effectiveness, demonstrating its capability to accurately identify and distinguish 3D faces at various scales. This work provides a promising solution for advanced biometric systems, especially for those which require 3D scale-invariant recognition.
