Image Sharpness-Based System Design for Touchless Palmprint Recognition

*Xu Liang, Zhaoqun Li, Jinyang Yang and David Zhang*

### **Abstract**

Currently, many palmprint acquisition devices have been proposed, but how to design the systems are seldom studied, such as how to choose the imaging sensor, the lens, and the working distance. This chapter aims to find the relationship between image sharpness and recognition performance and then utilize this information to direct the system design. In this chapter, firstly, we introduce the development of recent palmprint acquisition systems and abstract their basic frameworks to propose the key problems needed to be solved when designing new systems. Secondly, the relationship between the palm distance in the field of view (FOV) and image pixels per inch (PPI) is studied based on the imaging model. Suggestions about how to select the imaging sensor and camera lens are provided. Thirdly, image blur and depth of focus (DOF) are taken into consideration; the recognition performances of the image layers in the Gaussian scale space are analyzed. Based on this, an image sharpness range is determined for optimal imaging. The experiment results are obtained using different algorithms on various touchless palmprint databases collected using different kinds of devices. They could be references for new system design.

**Keywords:** palmprint recognition, system design, image sharpness assessment, scale space, field of view, depth of focus

#### **1. Introduction**

Biometric identification has been widely applied in modern society, such as electronic payment, entrance control, and forensic identification. As a reliable solution for identity authentication, biological characteristics refer to the inherent physiological or behavioral characteristics of the human body, including the iris, pattern, retina, palmprint, fingerprint, face and also voiceprint, gait, signature, key strength, etc. In the last decade, we have witnessed the successful employment of recognition systems using fingerprint, iris, and face. With the development of image capture devices and recognition algorithms, palmprint recognition receives more and more attention recently. Palmprint image contains principal lines, wrinkles, ridges, and texture that are regarded as useful features for palmprint representation and can be captured with a low-resolution image [1]. Palmprint recognition has several advantages compared with other biometrics: (1) the line features and texture features in a palmprint are discriminative and robust, which

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

the custom acquisition devices, more information can be retrieved in a multispectral image or 3D palmprint image. A 2D gray scale palmprint example with feature definitions is shown in **Figure 1**. The purpose of this chapter is to review recent research on palmprint acquisition systems to trace the development of palmprint recognition-based biometric systems. In this chapter, we coarsely divide the devices into three types by acquisition mode: touch-based devices, touchless devices, and portable devices. Touch-based devices usually have pegs to constrain the hand pose and position, which can capture the details of palmprint to the most extent. The illuminating environment is also stable during capturing process. These constrains ensure the captured palmprint images to be high quality. For touchless devices, users can freely place their palms in front of the camera while the hand pose is generally required to spread out the fingers. The environment during the capturing process becomes more complicated, especially the illumination. There are also datasets composed of palmprint images captured in a relatively free fashion. These images may be collected on the Internet which we will not discuss here. Otherwise, collectors use digital cameras or phone cameras to capture palmprint image, and usually, there are no strict conditions forced on the user. In the rest of this chapter, first, we will introduce the representative palmprint acquisition devices, and then study the relationship between the palm distance, image sharpness, hardware parameters, and the final recognition performance. **Table 1** summarizes the

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

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

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

palmprint acquisition devices.

**2.1 Touch-based devices**

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**2. The current palmprint recognition devices**

**Figure 1.** *Palmprint images and feature definitions.*

