An accurate multi-modal biometric identification system for person identification via fusion of face and finger print

Person identification plays a critical role in a large range of applications. Recently, RF based person identification becomes a hot research topic due to the contact-free nature of RF sensing that is particularly appealing in current COVID-19 pandemic. However, existing systems still have multiple limitations: i) heavily rely on the gait patterns of users for identification; ii) require a large amount of data to train the model and also extensive retraining for new users and iii) require a large frequency bandwidth which is not available on most commodity RF devices for static person identification. This paper proposes RF-Identity, an RFID-based identification system to address the above limitations and the contribution is threefold. First, by integrating walking pattern features with unique body shape features (e.g., height), RF-Identity achieves a high accuracy in person identification. Second, RF-Identity develops a data augmentation scheme to expand the size of the training data set, thus reducing the human effort in data collection. Third, RF-Identity utilizes the tag diversity in spatial domain to identify static users without a need of large frequency bandwidth. Extensive experiments show an identification accuracy of 94.2% and 95.9% for 50 dynamic and static users, respectively.

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Multimodal biometric identification system for mobile robots combining human metrology to face recognition and speaker identification

The 23rd IEEE International Symposium on Robot and Human Interactive Communication ◽

10.1109/roman.2014.6926273 ◽

2014 ◽

Cited By ~ 1

Author(s):

Simon Ouellet ◽

Francois Grondin ◽

Francis Leconte ◽

Francois Michaud

Keyword(s):

Face Recognition ◽

Mobile Robots ◽

Speaker Identification ◽

Biometric Identification ◽

Identification System

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Interactive Biometric Identification System Based on the Keystroke Dynamic

Biometric Identification Technologies Based on Modern Data Mining Methods ◽

10.1007/978-3-030-48378-4_3 ◽

2020 ◽

pp. 39-58

Author(s):

Stepan Bilan ◽

Mykola Bilan ◽

Andrii Bilan

Keyword(s):

Biometric Identification ◽

Identification System ◽

Keystroke Dynamic

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EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications

Computational Intelligence and Neuroscience ◽

10.1155/2022/5974634 ◽

2022 ◽

Vol 2022 ◽

pp. 1-18

Author(s):

Zaid Abdi Alkareem Alyasseri ◽

Osama Ahmad Alomari ◽

Mohammed Azmi Al-Betar ◽

Mohammed A. Awadallah ◽

Karrar Hameed Abdulkareem ◽

...

Keyword(s):

Search Algorithm ◽

Cuckoo Search ◽

Relevant Information ◽

Channel Selection ◽

Cuckoo Search Algorithm ◽

Identification System ◽

Healthcare Applications ◽

Person Identification ◽

Research Areas ◽

Identification Technique

Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain’s electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86 % using only 24 sensors with AR 20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.

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VARIATION DIFFERENCES OF PHALANX DISTAL FINGER PRINT PATTERN ON KLEPTOMANIA PATIENS AND NON-PATIENTS IN POLICE RESORT JEMBER

Jurnal Ilmu Kesehatan ◽

10.30650/jik.v8i1.1354 ◽

2020 ◽

Vol 8 (1) ◽

pp. 34-43

Author(s):

Amalia Rozaiza Ightikhoma ◽

Nazaratun Thaiyibah ◽

Ahmad Yudianto

Keyword(s):

Severe Trauma ◽

Identification System ◽

Unique Pattern ◽

Finger Print ◽

The Third ◽

Radial Loop ◽

Ulnar Loop ◽

Fingerprint Pattern

Phalanx distal pattern is a curve appeared and formed an unchangeable pattern unless it is caused by a severe trauma. Phalanx distal patterns are classified into 9, namely, Plain Arch, Tentarch, Ulnar loop, and Accidental. In order to find out suspect’s identity, Indonesia Autometic Print Identification System (INAFIS) used 3 stages, which one of them is to determine pattern variation. Phalanx distal fingerprint on human can be utilized as to identify a person, whereas no one has resembled phalanx distal fingerprint even on twins. In this research, the researcher analyzed tendentious pattern variation of phalanx distal pattern on kleptomaniac in police resort office Jember. Generally, phalanx distal fingerprint pattern found on kleptomaniac tendentiously has unique pattern plain arch as the third most apparent pattern beside unlar loop and whorl, meanwhile it was found radial loop on non-patient as the third most apparent beside unlar loop and whorl which generally was phalanx distal fingerprint.

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