Palmprint classification using principal lines

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.

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.

International Journal of Computer Applications, 2012

Palm print is a widely accepted biometric trait for authentication due to clarity in discriminating the features of palm such as large distance among non-class samples as well as minimum distance between intra-class samples. Area beneath the finger and enclosed by heart line is called as triradiated region or inter-distal region. This specific area of palm contains features which are unique and universally discriminating. In this paper we present a simple method to extract ridges in tri-radiated section also called as inter-distal region of palm. Different orientation of ridges extracted from inter-distal region appears as a fine texture. We use these fine textures for validating samples of Palm print. Reduction in size of the image, it's optimal storage, retrieval, computational efficiency without compromising the fine features of the palm sample and use of simple discriminating features to validate a given palm sample has motivated this paper. The results confirm the proposed methodology in this paper is most efficient one.

2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies, 2011

In these days Biometric based personnel authentication is evolving as robust method for security. A Palm print is one such reliable biometric entity showcasing all discriminating features. Reducing computational overhead is a challenge in palm print based biometric authentication system. In this paper we examined a new method for preliminary classification of palm print. An algorithm is proposed to implement proposed classification scheme. Experimentation and Test Results demonstrate classifying palm prints had been efficient using the proposed method.

Journal of Al-Qadisiyah for Computer Science and Mathematics

Todays, there are various of systems that requires high-level security methods. Due to the sophisticated methods of breaking the traditional security methods. One of the most advanced methods nowadays is handprint validation. Which is based on the features of the palm in hands. These feature could include the lines, valleys, hand texture, and other features. In this work, a survey of the latest works that are used for palm print detection and recognition

World Applied Sciences Journal

Palmprint is one of the main biometric features that has gained a lot of attention recently due to its commercial applications. Palmprint is more flexible in identifying an individual based on wrinkles, ridges and Principal lines. These features do not change during the lifetime of a person and no two individuals have similar palmprints. Most of the conventional methods use edge detection operators to detect lines in the palm which produce too many trivial lines. In this paper, a novel technique has been proposed to extract principal lines in two phases without using edge detection. The first phase involves some preprocessing operations and the second phase consists of morphological operations and elimination of undesirable components. The experimental results show that the proposed technique is more effective than existing techniques.

⎯ The palm was used in fortune telling 3000 years ago. Thus, During this period, many different problems related to palmprint recognition have been addressed. In the recent years, the palm print has been used for biometric applications as human verification and identification. The palm print has many features comparing with a fingerprint, The palm print has number of lines. One group of these lines is known as the principle lines which contains three lines(head line, heart line and life line). The lines are extracted from palm print image by edge detection algorithm which is implementing on ROI of palm print. The main goal of edge detection algorithm is to produce a line and extract important features and reduce the amount of data in the image. This paper investigates the several edge detection methods such as Sobel, Prewitt, Roberts, LOG, and Canny. In addition, we used edge detection using local entropy information and local variance. The experiment is tested on samples taken from four palm print databases (CASIA, PolyU, IIT and database available online). The analysis work has been performed by using PSNR and MSE of resultant images on these popular edge detection methods which improve the palm print matching process. The Prewitt, Roberts and LOG edge detection methods ignore the small lines and identify only the main longer lines while the Sobel identifies the medium and longer lines. The canny edge detection algorithm identifies the complete set of edges of various sizes. From experiment it was seen that good result found with an online database and polyU database by classical edge detection methods.

… Recognition, 2004

Various techniques in analyzing palmprint have been proposed but to the best of our knowledge, none has been studied on the selection and division of the region-ofinterest (ROI). Previous methods were always applied only to a fixed size square region chosen as the central part of the palm, which were then divided into square blocks for extraction of local features. In this paper, we proposed a new method in locating and segmenting the ROI for palmprint analysis, where the selected region varies with the size of the palm. Instead of square blocks, the region is divided into sectors of elliptical half-rings, which are less affected by misalignment due to rotational error. More importantly, our arrangement of the feature vectors ensures that only features extracted from the same spatial region of two aligned palms will be compared with each other. Encouraging results obtained favor the use of this method in the future development of palmprint analysis techniques.

International Journal of Biometrics

In this paper, palmprint verification and identification with minimum number of features is proposed. The wide principal line extractors (WPLEs) on the region of interest (ROI) are applied to generate wide principal line images (WPLIs). The WPLI is segmented into 2 × 2, 4 × 4, 8 × 8 and 16 × 16 and the feature value is extracted directly from each segment. Experiments are conducted by using the extracted features. The results show that the equal error rate (EER), decidability index (DI) and correct recognition rate (CRR) of the proposed approach is better than existing methods for PolyUPalmprint Database.

International Journal of Engineering Research and Advanced Technology (IJERAT), 2021

Physiological biometrics is one of the attractive fields for researchers due to its unique and stable features. One of these physical biometrics is "Palm Print". This new approach is used in personal recognition because of the powerful information that can be extracted from the palm print. The characteristics that are extracted keeping the rules of palmprint feature extraction are very important. In spite of the huge work done in this approach, the results recorded about the palm print are still uncompleted and the techniques that used in palm print feature extraction used in recognition are still continually modified. In this paper, we present a detailed background review for many techniques and methods that are used to extract features from palm print with many various methods and procedures. A comparison between these techniques is also presented.