FEATURE EXTRACTION METHODS FOR PALMPRINT RECOGNITION: A SURVEY AND EVALUATION

Abstract

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.

Existing System

Scale invariant feature transform (SIFT) and Gaussian curvature image (GCI) of 3-D palmprint.

Proposed System

The main contributions of this paper can be summarized as follows. 1) This paper groups palmprint images into four categories and provides a study of the feature extraction and recognition methods for all categories of palmprint images. Also, extensive experiments are conducted to compare and test the state of-the-art palmprint recognition methods on the widely used palmprint databases. 2) We implement the popular deep-learning methods on contactless palmprint recognition, the results of which validate the effectiveness of the deep-learning methods on palmprint recognition. 3) We offer several potential research directions for palmprint recognition in the future. In this paper, we first group palmprint images into four categories based on a unified standard, and then study the principles of the representative feature extraction and recognition methods for each category. Furthermore, we compare and evaluate the state-of-the-art methods on the widely used palmprint databases and offer some potential research directions.

Architecture

BLOCK DIAGRAM