**2. The current palmprint recognition devices**

#### **2.1 Touch-based devices**

can be easily fused with other hand features (dorsal hand vein, fingerprint, finger knuckle); (2) the pattern of palmprint is mainly controlled by genetic genes, when combined with palm vein information it can achieve high antispoof capability; (3) palmprint image acquisition is convenient and low-cost, and a relative lowresolution camera and a light source are sufficient to acquire the images; (4) the palmprint acquisition is hygienic and user friendly in the real applications. Based on

**Figure 1.**

*Biometric Systems*

**Table 1.**

**68**

*The palmprint recognition systems.*

*Palmprint images and feature definitions.*

[7] 2011 Touch-based Gray scale

[9] 2012 Touch-based Gray scale

**Ref. Year Device type Image type Description**

and IR

and 3D

[1] 2003 Touch-based Gray scale Adopt low-cost camera to capture low-resolution image

[2] 2007 Touchless RGB and IR Realize noncontact capturing of palmprint images under

[3] 2008 Touchless RGB Capture palm in real-time video stream using skin-color

[4] 2009 Touch-based 3D Acquire depth information in palm using structured light

[8] 2012 Portable Gray scale Use different portable devices to capture palmprint images

[10] 2015 Touchless RGB The RGB's blue and red channels are processed separately

[11] 2016 Touch-based Gray scale Develop a line scanner to capture palmprint images [12] 2017 Touch-based Gray scale Proposed a novel doorknob device to capture the knuckle

[13] 2018 Touchless Multispectral Capture palmprint and palm vein images in the device;

[5] 2010 Touch-based Multispectral Propose an online multispectral palmprint system [6] 2010 Touchless RGB and IR Capture palmprint and palm vein images simultaneously

palmprint; use pegs as guidance

unconstrained scenes

thresholding

imaging

Capture palmprint, palm vein, and dorsal vein images simultaneously

Acquire 3D information and 2D texture in palm

for bimodal feature extraction

images

established the current biggest publicly available database

Reference [1] is a pioneer work for palmprint acquisition and recognition that builds the first large-scale public palmprint dataset. The captured palmprint images are low-resolution with 75 pixels per inch (PPI), so that the whole process can be completed in 1 s, which achieves real-time palmprint identification. The palmprint capture device includes a ring light source, charge-coupled device (CCD) camera, a frame grabber, and an analog-to-digital (AD) converter. Six pegs are serving as control points that constrain the user's hands. To guarantee the image quality, during palmprint image capturing, the device environment is semiclosed, and the ring source provides uniform lighting conditions. After capturing the palmprint, the AD converter directly transmits the captured images by the CCD camera to a computer. The well-designed acquisition system can capture high-quality images, which boosts the performance of the identification algorithm. The experiment result also demonstrates that low-resolution palmprint can achieve efficient person identification. Our palms are not pure planes, and many personal characteristics lie on the palm surface. From this view, 2D palmprint recognition has some inherent drawbacks. On one hand, much 3D depth information is neglected in 2D imaging. The main features in 2D palmprint are line features including principal lines and wrinkles, which is not robust to the illumination variations and contamination influence. On the other hand, the 2D palmprint image is easy to be counterfeited so that the anti-forgery ability of 2D palmprint needs improvement. For capturing depth information in palmprint, [4, 14] explores a 3D palmprint acquisition system that leverages the structured light imaging technique. Compared to 2D palmprint images, several unique features, including mean curvature image, Gaussian curvature image, and surface type, are extracted in 3D images. Many studies have proposed different algorithms that encode the line features on the palm surface; however, the discriminative and antispoof capability of palm code needs to be further improved for large-scale identification. To obtain more biometric information in the palm, in [5] a multispectral palmprint acquisition system is designed, which can capture both red, green, and blue (RGB) images and near-infrared (NIR) images of one palm. It consists of a CCD camera, lens, an A/D converter, a multispectral light source, and a light controller. The monochromatic CCD is placed at the bottom of the device to capture palmprint images, and the light controller is used to control the multispectral light. In the visible spectrum, a three-mono-color LED array is used with red peaking at 660 nm, green peaking at 525 nm, and blue peaking at 470 nm. In the NIR spectrum, a NIR LED array peaking at 880 nm is used. It has been shown that light in the 700 to 1000 nm range can penetrate the human skin, whereas 880–930 nm provides a good contrast of subcutaneous veins. The system is low-cost, and the acquired palmprint images are high-quality. By fusing the information provided by multispectral palmprint images, the identification algorithm achieves higher performance on recognition accuracy and antispoof capacity.

**2.3 Portable devices**

devices are as follows:

**2.4 Key problems in device design**

*DOI: http://dx.doi.org/10.5772/intechopen.92828*

2.The focal length of the lens

5.The light source intensity

sufficient for palmprint identification.

**71**

3.The distance range of the palm

1.The resolution of the imaging sensor

4.The sharpness range of the final palmprint image

6.The signal-to-noise ratio of the palmprint image

With the widespread application of digital cameras and smartphones, more and more portable biometric devices appear to us. To investigate the problem of palmprint recognition across different portable devices and build the available dataset, [8] uses one digital camera and two smartphones to acquire palmprints in a free manner.

*Image Sharpness-Based System Design for Touchless Palmprint Recognition*

As is discussed above, the main parts of palmprint acquisition devices are cameras and light sources. So, the problems we need to consider when designing new

Many previous works have studied the light sources [15–17]. Generally, the basic goal is avoiding overexposure and underexposure. Image noise increases under low illumination conditions. Although many new deep learning-based denoising techniques are proposed [18], the most effective solution for palmprint imaging is developing active light sources to provide suitable illumination conditions. In this work, we only focus on the first four problems. We developed three palm image capture devices to test the performance of different hardware frameworks (as is shown in **Figure 2**). We denote them as *devicea*, *deviceb*, and *devicec*. Among them, *devicea* and *deviceb* are touch-based devices. *devicea* is designed to generate highquality palmprint images. The device contains an ultra-high-definition imaging sensor (about 500 M pixels) and a distortion-free lens. The long working distance is designed to further guarantee the image quality. During the capture process, the user's palm is put on the device to avoid motion blur. *deviceb* is designed to generate high-distortion palmprint images. It contains a high-definition imaging sensor (about 120 M pixels) and an ultrawide lens. The working distance is very short (about 2 cm). *devicec* is a touchless device; it is designed to capture high- and lowdefinition images in touchless scenarios. It has two cameras, one is high-definition (120 M pixels), and the other one is low-definition (30 M pixels); both of them are equipped with distortion-free lenses. We use different devices to collect palm images from the same palm; the captured images are shown in **Figure 2(d)**–**(e)**. We can see that the 500 M pixel camera can capture clear ridges and valleys of the palmprint, the 120 M pixel camera can capture most of the ridges and valleys, and the 30 M pixel camera only can capture the principal lines and coarse-grained skin textures. For touchless applications, the distance between the palm and the camera is not stable. Distance variations may decrease the palm image PPI and cause defocus-blur. In practice, it is very hard to guarantee the quality of the captured images. Hence, what we want to know is which level of image sharpness is
