High‐security photoacoustic identity recognition by capturing hierarchical vascular structure of finger

Abstract

Currently, most biometric methods mainly use single features, making them easily forged and cracked. In this study, a novel triple‐layers biometric recognition method, based on photoacoustic microscopy, is proposed to improve the security of biometric identity recognition. Using the photoacoustic (PA) dermoscope, three‐dimensional absorption‐structure information of the fingers was obtained. Then, by combining U‐Net, Gabor filtering, wavelet analysis and morphological transform, a lightweight algorithm called photoacoustic depth feature recognition algorithm (PADFR) was developed to automatically realize stratification (the fingerprint, blood vessel fingerprint and venous vascular), extracting feature points and identity recognition. The experimental results show that PADFR can automatically recognize the PA hierarchical features with an average accuracy equal to 92.99%. The proposed method is expected to be widely used in biometric identification system due to its high security.

3D Vascular Pattern Extraction from Grayscale Volumetric Ultrasound Images for Biometric Recognition Purposes

“…The majority of attention was devoted to fingerprints [10][11][12], but other characteristics like palmprints [13] and hand geometry [14][15][16] have also been investigated. Additionally, 3D vein patterns were extracted using power Doppler techniques [17][18][19] and photoacoustics [20][21][22][23]. However, even if quite accurate patterns were achieved, the acquisition time proved to be too long for both technologies.…”