The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

Review of Multimodal Biometric Identification Using Hand Feature and Face

In the era of Information Technology, openness of the information is a major concern. As the confidentiality and integrity of the information is critically important, it has to be secured from unauthorized access. Security refers to prohibit some unauthorized persons from some important data or from some precious assets. So we need accurateness on automatic personal identification in various applications such as ATM, driving license, passports, citizen's card, cellular telephones, voter's ID card etc. Unimodal system carries some problems such as Noise in sensed data, Intra-class variations, Inter-class similarities, Non-universality and Spoof attacks. The accuracy of system is improved by combining different biometric traits which are called multimodal. This system gives more accuracy as it would be difficult for imposter to spoof multiple biometric traits simultaneously. This paper reviews different methods for fusion of biometric traits

Person Identification Using Multimodal Biometrics under Different Challenges

The main aims of this chapter are to show the importance and role of human identification and recognition in the field of human-robot interaction, discuss the methods of person identification systems, namely traditional and biometrics systems, and compare the most commonly used biometric traits that are used in recognition systems such as face, ear, palmprint, iris, and speech. Then, by showing and comparing the requirements, advantages, disadvantages, recognition algorithms, challenges, and experimental results for each trait, the most suitable and efficient biometric trait for human-robot interaction will be discussed. The cases of human-robot interaction that require to use the unimodal biometric system and why the multimodal biometric system is also required will be discussed. Finally, two fusion methods for the multimodal biometric system will be presented and compared

IDENTITY RECOGNITION OPTIMIZATION BASED ON LBP FEATURE EXTRACTION

ABSTRACT Unimodal systems have limited information that can be used for identity recognition systems. The multimodal system was created to improve the unimodal system. The multimodal system used in this study is the combination of the face and palms at the matching score level. Matching scores is done using the Weighted Sum Rule method. Extract features from each sample using the Local Binary Pattern (LBP) method. Meanwhile, large data dimensions are reduced by using the Principal Component Analysis (PCA) method. The distance between face and palm data is measured using the closest distance, namely the Euclidean Distance method. Benchmark dataset using ORL, FERET and PolyU. Based on testing on each database, an accuracy rate of 98% (ORL and PolyU) and 95% (FERET and PolyU) is obtained. The test results show that the multimodal system using the Hybrid method (PCA and LBP) biometric system runs well and optimally. Keywords: Artificial intelegency, recognition, LBP, multimoda

A Review Of Multilevel Multibiometric Fusion System

Biometric systems allow automatic person recognition and authenticate based on the physical or behavioral characteristic. In recent years, researchers have paid close attention to the design of efficient multi-modal biometric systems due to their ability to withstand spoof attacks. Sometimes single biometric traits fail to extract relevant information for verifying the identity of a person. Therefore, combining multiple modalities, enhanced performance reliability could be achieved. If the security level increases then multi-level fusion techniques are used. This paper discusses the many fusion levels: algorithms, level of fusion, methods used for integrating the multiple verifiers and their applications

LEARNING-FREE DEEP FEATURES FOR MULTISPECTRAL PALM-PRINT CLASSIFICATION

The feature extraction step is a major and crucial step in analyzing and understanding raw data as it has a considerable impact on the system accuracy. Unfortunately, despite the very acceptable results obtained by many handcrafted methods, they can have difficulty representing the features in the case of large databases or with strongly correlated samples. In this context, we proposed a new, simple and lightweight method for deep feature extraction. Our method can be configured to produce four different deep features, each controlled to tune the system accuracy. We have evaluated the performance of our method using a multispectral palmprint based biometric system and the experimental results, using the CASIA database, have shown that our method has high accuracy compared to many current handcrafted feature extraction methods and many well known deep learning based methods

Multimodal Biometrics for Person Authentication

Unimodal biometric systems have limited effectiveness in identifying people, mainly due to their susceptibility to changes in individual biometric features and presentation attacks. The identification of people using multimodal biometric systems attracts the attention of researchers due to their advantages, such as greater recognition efficiency and greater security compared to the unimodal biometric system. To break into the biometric multimodal system, the intruder would have to break into more than one unimodal biometric system. In multimodal biometric systems: The availability of many features means that the multimodal system becomes more reliable. A multimodal biometric system increases security and ensures confidentiality of user data. A multimodal biometric system realizes the merger of decisions taken under individual modalities. If one of the modalities is eliminated, the system can still ensure security, using the remaining. Multimodal systems provide information on the “liveness” of the sample being introduced. In a multimodal system, a fusion of feature vectors and/or decisions developed by each subsystem is carried out, and then the final decision on identification is made on the basis of the vector of features thus obtained. In this chapter, we consider a multimodal biometric system that uses three modalities: dorsal vein, palm print, and periocular