Palmprint processes a number of unique features for reliable personal recognition. However, different types of palmprint images contain different dominant features. Instead, only some features of the palmprint are visible in a palmprint image, whereas the other features may not be notable. For example, the low-resolution palmprint image has visible principal lines and wrinkles. By contrast, the high-resolution palmprint image contains clear ridge patterns and minutiae points. In addition, the three dimensional (3-D) palmprint image possesses curvatures of the palmprint surface. So far, there is no work to summarize the feature extraction of different types of palmprint images. In this paper, we have an aim to completely study the feature extraction and recognition of palmprint. We propose to use a unified framework to classify palmprint images into four categories: 1) the contact-based; 2) contactless; 3) high-resolution; and 4) 3-D palmprint images. Then, we analyze the motivations and theories of the representative extraction and matching methods for different types of palmprint images. Finally, we compare and test the state-of-the-art methods via the widely used palmprint databases, and point out some potential directions for future research.

Feature Extraction Methods for Palmprint Recognition: A Survey and Evaluation

Various palmprint recognition methods have been proposed based on orientation features of palmprints. Among them, the competitive code method using the dominant orientation of palmprint images achieves promising performance in palmprint recognition. In this paper, we propose a discriminative and robust competitive code based method, which uses a more accurate dominant orientation representation of palmprint images for palmprint authentication. Moreover, we propose to weight the orientation information of a neighbor area to improve the precision and stability of the discriminative and robust dominant orientation code. Experiments performed on three types of palmprint databases and a noisy dataset validate the effectiveness of the proposed method.

Discriminative and Robust Competitive Code for Palmprint Recognition

Half-orientation extraction of palmprint features

With the rapid development of information storage and networking technologies, quintillion bytes of data are generated every day from social networks, business transactions, sensors, and many other domains. The increasing data volumes impose significant challenges to traditional data analysis tools in storing, processing, and analyzing these extremely large-scale data. For decades, hashing has been one of the most effective tools commonly used to compress data for fast access and analysis, as well as information integrity verification. Hashing techniques have also evolved from simple randomization approaches to advanced adaptive methods considering locality, structure, label information, and data security, for effective hashing. This survey reviews and categorizes existing hashing techniques as a taxonomy, in order to provide a comprehensive view of mainstream hashing techniques for different types of data and applications. The taxonomy also studies the uniqueness of each method and therefore can serve as technique references in understanding the niche of different hashing mechanisms for future development.

Hashing Techniques: A Survey and Taxonomy

Orientation features are successfully used in coding-based palmprint recognition methods. In this paper, we propose a discriminative neighboring direction indicator to represent the orientation feature of the palmprint. The neighboring direction indicator feature not only represents the most dominant orientation feature of the palmprint, but also better describes the orientation feature of those points which have double dominant orientations. In addition, the neighboring direction indicator shows good robustness to noise and rotation. Using the neighboring direction indicator, we propose a novel palmprint recognition method. Extensive experiments conducted on three types of palmprint databases demonstrate that the proposed method gives better performance than the existing state-of-the-art orientation-based methods. By using the proposed method, the equal error rate is improved by about 10% for palmprint verification, and the average error rate is reduced by 2.7–14% for palmprint identification with a single training sample.

Palmprint Recognition Using Neighboring Direction Indicator

In this paper, we present two hashing based techniques for fast palmprint identification. We first propose three properties required by a hash function and then introduce our first fast identification method based on orientation pattern (OP) hashing. We give the definition of OP and demonstrate that it meets all the requirements of the hash function, and thus, would be appropriate for hashing based fast palmprint identification. We then introduce the second fast identification method based on principal orientation pattern (POP) hashing. Because the POPs are constructed using more stable orientation features, POP hashing can find the target template more quickly thus causing earlier termination of the identification process. We evaluate our methods on the Hong Kong PolyU large-scale database (9667 palms) and the CASIA palmprint database (600 palms) plus a synthetic database (100 000 palms). Experimental results show that, on the Hong Kong PolyU large-scale database, the speedups of OP hashing and POP hashing over brute-force search are 16.93 and 19.91, respectively, and the identification accuracy is slightly higher. While on the CASIA database plus the synthetic database, the speedups of OP hashing and POP hashing are 8.03 and 15.67, respectively, and the identification accuracy almost remains the same. Results also show that, in terms of accuracy, our methods are comparable to several state-of-the-art palmprint identification approaches, while in terms of speed, our methods are much faster.

Hashing Based Fast Palmprint Identification for Large-Scale Databases

A palmprint identification system recognizes a query palmprint image by searching for its nearest neighbor from among all the templates in a database. When applied on a large-scale identification system, it is often necessary to speed up the nearest-neighbor searching process. In this paper, by viewing the palmprint feature as a high-dimension binary vector, we present a palmprint identification method using orientation pattern hashing. We propose three properties required by the hash function and demonstrate that the orientation pattern has all of these properties. Under some simple assumptions we give the parameter selection method for fast and accurate palmprint identification. Experimental results on the Hong Kong large scale database (9667 palms) show that the proposed method is over 16 times faster than brute force searching, while its accuracy is slightly higher. Evaluations on the CASIA palmprint database (600 palms) plus a synthetic database (100,000 palms) show a speedup of 6.8 over brute force searching and a negligible loss of accuracy.

Jiaqiang Wang

Papers

Hashing Based Fast Palmprint Identification for Large-Scale Databases

Fast Palmprint Identification Using Orientation Pattern Hashing