Elsevier

Pattern Recognition

Volume 42, Issue 7, July 2009, Pages 1408-1418
Pattern Recognition

A survey of palmprint recognition

https://doi.org/10.1016/j.patcog.2009.01.018Get rights and content

Abstract

Palmprint recognition has been investigated over 10 years. During this period, many different problems related to palmprint recognition have been addressed. This paper provides an overview of current palmprint research, describing in particular capture devices, preprocessing, verification algorithms, palmprint-related fusion, algorithms especially designed for real-time palmprint identification in large databases and measures for protecting palmprint systems and users’ privacy. Finally, some suggestion is offered.

Introduction

The inner surface of the palm normally contains three flexion creases, secondary creases and ridges. The flexion creases are also called principal lines and the secondary creases are called wrinkles. The flexion and the major secondary creases are formed between the third and fifth months of pregnancy [36] and superficial lines appear after we born. Although the three major flexions are genetically dependent, most of other creases are not [2]. Even identical twins have different palmprints [2]. These non-genetically deterministic and complex patterns are very useful in personal identification. Human beings were interested in palm lines for fortune telling long time ago. Scientists know that palm lines are associated with some genetic diseases including Down syndrome, Aarskog syndrome, Cohen syndrome and fetal alcohol syndrome [68]. Scientists and fortunetellers name the lines and regions in palm differently as shown in Fig. 1 [30].

Palmprint research employs either high or low resolution images. High resolution images are suitable for forensic applications such as criminal detection [24]. Low resolution images are more suitable for civil and commercial applications such as access control. Generally speaking, high resolution refers to 400 dpi or more and low resolution refers to 150 dpi or less. Fig. 2 illustrates a part of a high-resolution palmprint image and a low resolution palmprint image. Researchers can extract ridges, singular points and minutia points as features from high resolution images while in low resolution images they generally extract principal lines, wrinkles and texture. Initially palmprint research focused on high-resolution images [69], [70] but now almost all research is on low resolution images for civil and commercial applications. This is also the focus of this paper.

The design of a biometric system takes account of five objectives: cost, user acceptance and environment constraints, accuracy, computation speed and security (Fig. 3). Reducing accuracy can increase speed. Typical examples are hierarchical approaches. Reducing user acceptance can improve accuracy. For instance, users are required to provide more samples for training. Increasing cost can enhance security. We can embed more sensors to collect different signals for liveness detection. In some applications, environmental constraints such as memory usage, power consumption, size of templates and size of devices have to be fulfilled. A biometric system installed in PDA (personal digital assistant) requires low power and memory consumption but these requirements may not be vital for biometric access control systems. A practical biometric system should balance all these aspects.

A typical palmprint recognition system consists of five parts: palmprint scanner, preprocessing, feature extraction, matcher and database illustrated in Fig. 4. The palmprint scanner collects palmprint images. Preprocessing sets up a coordinate system to align palmprint images and to segment a part of palmprint image for feature extraction. Feature extraction obtains effective features from the preprocessed palmprints. A matcher compares two palmprint features and a database stores registered templates.

The rest of this paper is organized as follows: Section 2 reviews palmprint scanners and preprocessing algorithms, Section 3 lists verification algorithms, Section 4 summarizes various fusion approaches for enhancing verification accuracy, Section 5 discusses the algorithms for real-time palmprint identification in large databases, Section 6 mentions the existing methods for protecting palmprint systems and user privacy and Section 7 offers some concluding remarks and further directions.

Section snippets

Palmprint scanners

Researchers utilize four types of sensors: CCD-based palmprint scanners, digital cameras, digital scanners and video cameras to collect palmprint images. Fig. 5 shows a CCD-based palmprint scanner developed by The Hong Kong Polytechnic University. Zhang et al. and Han were the first two research teams developing CCD-based palmprint scanners [7], [9]. CCD-based palmprint scanners capture high quality palmprint images and align palms accurately because the scanners have pegs for guiding the

Verification algorithms

Once the central part is segmented, features can be extracted for matching. There are two types of recognition algorithms, verification and identification. Verification algorithms must be accurate. Identification algorithms must be accurate and fast (matching speed). This section concentrates on verification algorithms and identification algorithms will be discussed in Section 5. Verification algorithms are line-, subspace- and statistic-based. Some algorithms in this section can support a

Fusion

Fusion is a promising approach that may increase the accuracy of systems [77]. Many biometric traits including fingerprint [82], palm vein [84], finger surface [19], [39], [80], face [20], [62], [66], [81], iris [88], and hand shape [17], [39], [50], [61], [76] have been combined with palmprints at score level or at representation level. Combining other hand features such as hand geometry and finger surface with palmprints allows these features and palmprints to be extracted from a single hand

Classification and hierarchical approaches

The problem of real-time identification in large databases has been addressed in three ways: through hierarchies, classification and coding. Hierarchical approaches employ simple but computationally effective features to retrieve a sub-set of templates in a given database for further comparison [14], [15], [16]. These approaches increase matching speed at the cost of accuracy. Classifiers can remove target palmprints by using simple features.

Classification approaches assign a class to each

Security and privacy

Biometric systems are vulnerable to many attacks including replay, database and brute-force attacks [26]. Compared with verification, fusion and identification, there has been little research on palmprint security. We have analyzed the probability of successfully using brute-force attack to break in a palmprint identification system [5] and proposed cancelable palmprints for template re-issuance to defend replay attacks and database attacks [86]. Connie et al. combined pseudo-random keys and

Discussion and conclusion

Before the end of this paper, we would like to re-mention some papers that are very worthy to read carefully. Our first suggestion is Han's work [9], which is a very complete work. We especially appreciate his palmprint scanner described in this work that can collect images of whole hands and use pegs for hand placement. For verification, we recommend Hennings-Yeomans et al.'s correlation filter approach [97]. They employ many user-specific techniques to optimize accuracy. For real-time large

Acknowledgments

The authors would like to express their sincere gratitude to Michael Wong for his great contribution to palmprint research, especially data collection and palmprint scanner development and also to the anonymous reviewers for their constructive comments. The work is partially supported by the CERG fund from the HKSAR Government, the central fund from Hong Kong Polytechnic University, and the NSFC/863 funds under Contract no. 60620160097 and 2006AA01Z193 in China.

About the Author—ADAMS KONG finished his Ph.D. thesis, “Palmprint identification based on generalization of IrisCode” in the University of Waterloo, 2007. He developed several coding methods including PalmCode, Fusion Code and Competitive Code for high-speed palmprint identification. In addition to identification, he also completed a study about twin's palmprint and proposed security measures for palmprint systems. Although palmprint recognition is his major research area, the best of his work

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    About the Author—ADAMS KONG finished his Ph.D. thesis, “Palmprint identification based on generalization of IrisCode” in the University of Waterloo, 2007. He developed several coding methods including PalmCode, Fusion Code and Competitive Code for high-speed palmprint identification. In addition to identification, he also completed a study about twin's palmprint and proposed security measures for palmprint systems. Although palmprint recognition is his major research area, the best of his work may be the analysis and generalization of IrisCode. Except his professional research, recently, he puts a great focus on global warming. He expresses the messages to his students. More information about Adams Kong can be found at http://www.ntu.edu.sg/home/adamskong/.

    About the Author—DAVID ZHANG graduated in Computer Science from Peking University in 1974. In 1983 he received his M.Sc. in Computer Science and Engineering from the Harbin Institute of Technology (HIT) and then in 1985 his Ph.D. from the same institution. From 1986 to 1988 he was first a Postdoctoral Fellow at Tsinghua University and then an Associate Professor at the Academia Sinica, Beijing. In 1994 he received his second Ph.D. in Electrical and Computer Engineering from the University of Waterloo, Ontario, Canada. Currently, he is a Chair Professor, the Hong Kong Polytechnic University where he is the Founding Director of the Biometrics Technology Centre (UGC/CRC) supported by the Hong Kong SAR Government. He also serves as Adjunct Professor in Tsinghua University, Shanghai Jiao Tong University, Harbin Institute of Technology, Beihang University and the University of Waterloo. Professor Zhang's research interests include automated biometrics-based authentication, pattern recognition, and biometric technology and systems. He is the Founder and Editor-in-Chief, International Journal of Image and Graphics (IJIG) (www.worldscinet.com/ijig/ijig.shtml); Book Editor, Kluwer International Series on Biometrics (KISB) (www.wkap.nl/prod/s/KISB); Chairman, Hong Kong Biometric Authentication Society and Program Chair, the First International Conference on Biometrics Authentication (ICBA), Associate Editor of more than ten international journals including IEEE Trans on SMC-A/SMC-C, Pattern Recognition, and is the author of more than 140 journal papers, twenty book chapters and eleven books. As a principal investigator, Professor Zhang's has since 1980 brought to fruition many biometrics projects and won numerous prizes. In 1984 his Fingerprint Recognition System won the National Scientific Council of China's third prize and in 1986 his Real-Time Remote Sensing System took the Council's first prize. In 2002 his Palmprint Identification System won a Silver Medal at the Seoul International Invention Fair, following that in 2003 by taking a Special Gold Award, a Gold Medal, and a Hong Kong Industry Award. Professor Zhang holds a number of patents in both the USA and China and is a Croucher Senior Research Fellow and Distinguished Speaker of IEEE Computer Society.

    About the Author—MOHAMED S. KAMEL received the B.Sc. (Hons) EE (Alexandria University), M.A.Sc. (McMaster University), Ph.D. (University of Toronto) He is at present Professor and Director of the Pattern Analysis and Machine Intelligence Laboratory at the Department of Electrical and Compute Engineering, University of Waterloo, Canada. Professor Kamel holds Canada Research Chair in Cooperative Intelligent Systems. Dr. Kamel's research interests are in Machine Intelligence, Image Analysis and Pattern Recognition. He has authored and co-authored over 300 papers in journals, and conference proceedings, 2 patents and numerous technical and industrial project reports. Under his supervision, 63 Ph.D. and M.A.Sc. students have completed their degrees. He is the Editor-in-Chief of the International Journal of Robotics and Automation, Associate Editor of the IEEE SMC, Part A, the Intelligent Automation and Soft Computing, Pattern Recognition Letters, and member of the editorial board of the International Journal of Image and Graphics. Dr. Kamel is member of ACM, AAAI, APEO and Fellow of the IEEE. He served as consultant for General Motors, NCR, IBM, Northern Telecom and Spar Aerospace. He is member of the board of directors and co-founder of Virtek Vision International in Waterloo.

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