Palmprint verification based on principal lines

Abstract

In this paper, we propose a novel palmprint verification approach based on principal lines. In feature extraction stage, the modified finite Radon transform is proposed, which can extract principal lines effectively and efficiently even in the case that the palmprint images contain many long and strong wrinkles. In matching stage, a matching algorithm based on pixel-to-area comparison is devised to calculate the similarity between two palmprints, which has shown good robustness for slight rotations and translations of palmprints. The experimental results for the verification on Hong Kong Polytechnic University Palmprint Database show that the discriminability of principal lines is also strong.

Introduction

In networked society, automatic personal verification is a crucial problem that needs to be solved properly. And in this field biometrics is one of the most important and effective solutions. Recently, palmprint based verification systems (PVS) have been receiving more attention from researchers [1]. Compared with fingerprint or iris based personal verification systems which have been widely used [2], [3], the PVS can also achieve satisfying performance. For example, it can provide reliable recognition rate with fast processing speed [1]. Particularly, the PVS has several special advantages such as rich texture feature, stable line feature, low-resolution imaging, low-cost capturing devices, and easy self-positioning, etc.

So far, there have been many approaches proposed for palmprint verification/identification, which can be mainly divided into five categories: (1) texture based approaches [1], [4]; (2) appearance based approaches [5], [6], [7], [8]; (3) multiple features based approaches [9]; (4) orientation based approaches [10], [11]; and (5) line based approaches [12], [13], [14], [15], [16], [17], [18]. The main approaches based on texture are to extract texture feature by exploiting 2-D Gabor filter, which have been shown to be of satisfying performance in terms of recognition rate and processing speed [1], [4]. Appearance based approaches were also reported to achieve exciting results in many literatures, but they may be sensitive to illumination, contrast, and position changes in real applications. In addition, it was reported in Ref. [9] that multiple features based approaches using information fusion technology could provide more reliable results. Recently, orientation codes are deemed to be the most promising methods, since the orientation feature contains more discriminative power than other features, and is more robust for the change of illumination.

Obviously, line is the basic feature of palmprint. Thus, line based approaches also play an important role in palmprint verification/identification field. Zhang et al. used overcomplete wavelet expansion and directional context modeling technique to extract principal lines-like features [12]. Han et al. proposed using Sobel and morphological operations to extract the line-like features from palmprint images obtained using a scanner [13]. Lin et al. applied the hierarchical decomposition mechanism to extract principal palmprint features from a region of interest (ROI), which includes directional and multi-resolution decompositions [14]. However, these methods cannot extract palm lines explicitly. Additionally, Wu et al. and Liu et al. proposed two different approaches based on palm lines, which will be discussed in later section [15], [16], [17], [18].

It is well known that palm lines consist of wrinkles and principal lines. And principal lines can be treated as a separate feature to characterize a palm. Therefore, there are several reasons to carefully study principal lines based approaches. At first, principal lines based approaches can be jointly considered with the person's habit. For instance, when human beings are comparing two palmprints, they instinctively compare principal lines. Secondly, principal lines are generally more stable than wrinkles. The latter is easily masked by bad illumination condition, compression, and noise. Thirdly, principal lines can act as an important component in multiple features based approaches. Fourthly, in some special cases, for example, when the police is searching for some palmprints with similar principal lines, other features cannot be used to replace principal lines. At last, principal lines can be used in palmprint classification or fast retrieval schemes. However, principal lines based approaches have not been studied adequately so far. The main reason is that it is very difficult to extract principal lines from complex palmprint images, which contain many strong and long wrinkles. At the same time, many researchers claimed that it was difficult to obtain a high recognition rate using only principal lines because of their similarity among different people [1]. In other words, they thought the discriminability of principal lines was limited. Nevertheless, they did not conduct related experiments to verify their viewpoints.

In this paper, we propose a novel palmprint verification approach based on principal lines, and further discuss the discriminability of principal lines. Here, before presenting the proposed approach, we shall first present the definition of principal lines used in the whole paper. To illustrate this definition, three typical palmprint images are shown in Fig. 1. Generally speaking, most palmprints have three principal lines: heart line, head line, and life line, which are longest, strongest, and widest lines in palmprint image, and have stable line initials and positions (see Fig. 1(a)) [15]. In addition, a lot of palmprints may have more or less principal lines due to their diversity and complexity (see Fig. 1(b)). In this paper, assuming that within a few of palmprints one or two longest and strongest wrinkles that have similar directions to three principal lines are also regarded as a part of principal lines (see Fig. 1(c)).

In principal lines extraction stage, what are criterions for distinguishing principal lines from wrinkles is an important issue. Through careful observation and analysis, we adopt two main differences between principal lines and wrinkles as the corresponding criterions. The one is the line energy of which principal lines are stronger than that of wrinkles. Another one is the direction of which most wrinkles obviously differ from that of principal lines. In addition, since the Radon transform and its variations are powerful tools to detect the directions and energies of lines in an image, they are used in our method. In matching stage, we devise a matching algorithm based on pixel-to-area comparison to calculate the similarity between two palmprints, which have shown good robustness for slight rotations and translations.

Additionally, what we must stress here is that in this paper, all palmprint images are obtained from Hong Kong Polytechnic University Palmprint Database [19], which were captured by a CCD-based device described in Ref. [1]. This paper is organized as follows. Section 2 presents the method of principal line extraction. Section 3 gives the palmprint matching method based on pixel-to-area comparison. Section 4 reports the experimental results including principal lines extraction, verification, and computational time. Section 5 discusses the discriminability of principal lines. And Section 6 concludes the whole paper with some conclusive remarks.