Palmprint Recognition: A Naïve Bayesian Approach

European Journal of Electrical Engineering and Computer Science, 2020

The presented paper proposes an algorithm for palmprint recognition using seven different machine learning algorithms. First of all, we have proposed a region of interest (ROI) extraction methodology which is a two key points technique. Secondly, we have performed some image enhancement techniques such as edge detection and morphological operations in order to make the ROI image more suitable for the Hough transform. In addition, we have applied the Hough transform in order to extract all the possible principle lines on the ROI images. We have extracted the most salient morphological features of those lines; slope and length. Furthermore, we have applied the invariant moments algorithm in order to produce 7 appropriate hues of interest. Finally, after performing a complete hybrid feature vectors, we have applied different machine learning algorithms in order to recognize palmprints effectively. Recognition accuracy have been tested by calculating precision, sensitivity, specificity,...

2007

In this paper, we propose an efficient palmprint recognition scheme which has two features: 1) representation of palm images by two dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalized classification method based on Kernel Principal Component Analysis (Kernel PCA). Wavelet subband coefficients can effectively capture substantial palm features while keeping computational complexity low. We then kernel transforms to each possible training palm samples and then mapped the high-dimensional feature space back to input space. Weighted Euclidean linear distance based nearest neighbor classifier is finally employed for recognition. We carried out extensive experiments on PolyU Palmprint database includes 7752 palms from 386 different palms. Detailed comparisons with earlier published results are provided and our proposed method offers better recognition accuracy (99.654%).

Image and Vision Computing, 2005

Recently, biometric palmprint has received wide attention from researchers. It is well-known for several advantages such as stable line features, low-resolution imaging, low-cost capturing device, and user-friendly. In this paper, an automated scanner-based palmprint recognition system is proposed. The system automatically captures and aligns the palmprint images for further processing. Several linear subspace projection techniques have been tested and compared. In specific, we focus on principal component analysis (PCA), fisher discriminant analysis (FDA) and independent component analysis (ICA). In order to analyze the palmprint images in multi-resolution-multifrequency representation, wavelet transformation is also adopted. The images are decomposed into different frequency subbands and the best performing subband is selected for further processing. Experimental result shows that application of FDA on wavelet subband is able to yield both FAR and FRR as low as 1.356 and 1.492% using our palmprint database. q

2017

Biometrics is defined as the exceptional or individual physical properties or attributes of human body. These attributes are used to recognize each human. Any details of the person body which differs from another person to the other is used as unique biometrics data which serve to that’s person’s unique ID. Palmprint recognition being one of the important aspects of biometric technology. These palmprint recognition serves into four stages, palmprint image acquisition, preprocessing, feature extraction and matching. The major approach for palmprint recognition is to extract feature vector from each individual palm and to perform matching based on some distance metrics. This paper present a detailed review on palmprint recognition approaches.

2014

In the networked society there are a great number of systems that need biometric identification ,Biometrics is known to offer a reliable and natural solution for authentication purpose. Palmprint is one new Biometric which solves problems related to Automatic and Authentication recognition as compare to other Biometrics.It contains principle lines ,wrinkles and ridges on the surface of palm. These structure are stable and remain unchanged throughput life of an individual.. In this paper we have proposed palmprint recognition system. Palm is inner region between the hand wrist and the base of the fingers. Here, Local Binary Pattern (LBP) and its analysis are discussed for palm print recognitionwe also included Robust line orientation code (RLOC) for plamprint verification. This technique is simple, highly accurate and takes less time to process the palmprint image..

Journal of emerging technologies and innovative research, 2019

Palm print recognition has risen as a profoundly acknowledged biometric framework because of its simple acquisition and unwavering quality. Palm is the internal surface of hand among wrist and fingers. The inward surface of palm contains three flexion wrinkles, optional wrinkles, and edges for each finger. The flexion is additionally called as chief lines and auxiliary wrinkles are called wrinkles. Palm feature likewise incorporates particular focuses, edges, wrinkles, and delta, datum and particulars focuses. Palm features are remarkable for each person and have rich data that can be utilized for feature extraction. The palm lines and wrinkles are shaped during third and fifth month of the development of baby. An area of interest (ROI) is separated from the palm region for handling. Palm recognition process incorporates feature extraction (put away as layout in the database) coordinating (input question features are coordinated with put away features) and choice making (to acknowledge or dismiss the inquiry dependent on coordinate score). In this part a review of palm print recognition framework, handling stages and approaches is displayed.

2nd International Conference on Research in Science, Engineering and Technology (ICRSET’2014), 2014

This paper proposes an algorithm for biometric palm recognition of an individual using geometrical and texture properties. Biometrics is physiological characteristics of human beings, unique for every individual that are usually time invariant & easy to acquire. Palmprint is one of the physiological biometrics due to its stable & unique characteristics. The physical dimensions of a human palm contains information that is capable of authenticating the identity of an individual. In this study algorithm is proposed to extract a finger length as geometrical feature and principal lines as texture feature. Simulation results shows false accept rate (FAR) is 25%, false reject rate (FRR) as 18.75%, genuine acceptance rate (GAR) 81.25%, half total error rate (HTER) 21.87% and accuracy 78.12% in case of geometrical features whereas accuracy is 92.3% in case of texture feature.

Abstract— Palmprint recognition has been investigated over the past decade. Palmprint recognition has five stages palmprint acquisition, pre-processing, feature extraction, enrolment (database) and matching. Due to rich information in palmprint it became a powerful means in person identification. The major approach for palmprint recognition is to extract feature vectors corresponding to individual palm image and to perform matching based on some distance metrics. Palmprint recognition is a challenging problem mainly due to low quality of pattern, large nonlinear distortion between different impression of same palm and large image size, which makes feature extraction and matching computationally demanding. In this paper we talk about the various approaches of palmprint recognition using matching pattern method.

Journal of Al-Nahrain University of Science

Biometric system is considered of an important type of security systems nowadays, because it relays on the individual traits (physical or behavioral) non-participation between any two people that can't be lost on lifetime and can't be stolen. This paper, will present the individual classification system based on hand geometry and palm texture feature where it is one of the parts of the human body, which has an impressive set of information capable to distinguish and identify individuals. Utilize Principal Component Analysis PCA for palimprint texture feature extraction. The proposed system consists of three phases: image preprocessing, hand feature extraction and pattern classification. Utilize Principal Component Analysis PCA for palimprint texture feature extraction. The proposed system utilized complete hand image inside database consists of 600 pictures, include 100 people, each one has six images. Experimental results show that 98.3% is achieved and that illustrate the applicability of the system in the security's average of different environments.

2014

I. INTRODUCTION Biometric based recognition systems have been receiving much attention in the field of personal identification/verification over the past decade. Based on the traits used for identification, biometrics is broadly classified into two categories such as behavioral and physiological as shown in fig. 1. In biometric system, physiological characteristics are considered to be more reliable compared to the behavioral characteristics. Palmprint comes under the physiological category. Palmprint focuses on the inner surface of a hand, its pattern of lines and the shape of the surface. Palmprint contains more amounts of detail in terms of principal lines, wrinkles and creases Fig.1: Biometric classifications