3D palmprint and hand imaging system based on full-field composite color sinusoidal fringe projection technique

Abstract

Palmprint and hand shape, as two kinds of important biometric characteristics, have been widely studied and applied to human identity recognition. The existing research is based mainly on 2D images, which lose the third-dimensional information. The biological features extracted from 2D images are distorted by pressure and rolling, so the subsequent feature matching and recognition are inaccurate. This paper presents a method to acquire accurate 3D shapes of palmprint and hand by projecting full-field composite color sinusoidal fringe patterns and the corresponding color texture information. A 3D imaging system is designed to capture and process the full-field composite color fringe patterns on hand surface. Composite color fringe patterns having the optimum three fringe numbers are generated by software and projected onto the surface of human hand by a digital light processing projector. From another viewpoint, a color CCD camera captures the deformed fringe patterns and saves them for postprocessing. After compensating for the cross talk and chromatic aberration between color channels, three fringe patterns are extracted from three color channels of a captured composite color image. Wrapped phase information can be calculated from the sinusoidal fringe patterns with high precision. At the same time, the absolute phase of each pixel is determined by the optimum three-fringe selection method. After building up the relationship between absolute phase map and 3D shape data, the 3D palmprint and hand are obtained. Color texture information can be directly captured or demodulated from the captured composite fringe pattern images. Experimental results show that the proposed method and system can yield accurate 3D shape and color texture information of the palmprint and hand shape.

3D Palmprint Recognition Using Dempster-Shafer Fusion Theory

“…Suppose that Compute basic belief function m 2 (B j ) Figure 6: Flow chart of palmprint recognition based on MC and GC data fusion. The fusion belief function was determined by (13). Palmprint recognition was accomplished according to the recognition rules in Section 4.3.…”

“…Palmprint images can be acquired in a similar manner to face images and a number of researchers have suggested that the human palmprint is unique enough to each individual to allow practical use as a biometric. Several researchers have made palmprint recognition based on the palmprint's appearance in 2D intensity images [1][2][3][4][5][6][7][8][9][10], whereas a smaller number of researchers have accomplished palmprint recognition using 3D palmprint information [11][12][13]. Although 2D palmprint recognition techniques can achieve high accuracy, the 2D palmprint recognition may be liable to strong illumination variations and condition changes of palmprint and much 3D palm structural information is lost in the 2D palmprint acquisition process.…”

Light-Field-Assisted Phase Unwrapping of Fringe Projection Profilometry

“…F RINGE projection profilometry (FPP) is used extensively in optical three-dimensional (3D) measurements credit to its high resolution, precision, and speed. FPP usually projects sinusoidal fringes to obtain the information modulated by the surface of the object [1]- [5], the phase distribution is wrapped to the principal value ranging between −π and π. Consequently, the phase unwrapping must be carried out to obtain a continuous phase map. Commonly used phase unwrapping algorithms can be divided into two categories, spatial phase unwrapping [6]- [9] and temporal phase unwrapping [10]- [13].…”

Dynamic Phase Measuring Profilometry Based on Tricolor Binary Fringe Encoding Combined Time-Division Multiplexing

“…Optical three dimensional (3D) shape measurement based on fringe projection is widely used in many fields such as industry inspection [1], 3D thermal deformation measurement of electronic devices [2], 3D point cloud reconstruction [3], 3D printing [4], face recognition [5] and so on with its advantages of noncontacting measurement, full-field acquisition, high precision, and ease of information processing. The traditional static or online 3D shape measurement methods based on fringe projection may be hard to satisfy the demand of real-time or dynamic 3D shape measurement.…”