User Recognition of Each Personal Identification Technique based on the Biometrics

This paper introduces an efficient Eigen values based technique for online iris image compression and identification of a human including the case of identical twins. The iris image is extracted after removing the pupil, eye brow, skin and other noise disturbances from an actual image. The extracted iris image is divided into different blocks of size of 16 × 16. Now, Eigen values are calculated for each block and these Eigen values are stored in the smart card memory for further identification. Therefore, when checking if two iris images are identical or not, all we need is to compare the stored Eigen values with online calculated Eigen values. If two iris images have the same Eigen values, this means that both iris images belong to the same person. In our research, we have concluded that iris images of different persons have different Eigen values, including the case of identical twins. We conducted experiments on CASIA and Multimedia University iris image databases and we found that our Eigen Values Based Iris Image Identification Technique is giving 99.99% accuracy for the same image of identical twins and individuals. The implementation leads us to believe that our method is giving the best matching result in the case of identical twins and individuals. It is an efficient, secure and economically feasible approach for online personal identification.

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Novel Distance Metric for Touch less Footprint Based Identification Technique

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.c7967.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 1011-1016

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Personal Identification ◽

Shape Features ◽

Distance Metric ◽

Mahalanobis Distances ◽

Behavioral Attribute ◽

Identification Technique ◽

Pca Method ◽

Personal Recognition

In this fast-paced technology-driven today's era, biometrics is not the new buzzword in the information security domain. Biometrics uses any physiological or/ and behavioral attribute/s of an individual for personal identification and/or verification. In biometrics, so many traits, like a fingerprint, face, palm, retina, iris, ECG, gait, voice, and signature, etc., have been used from ages to uniquely identify a human being. Biometrics based on Footprints is the latest practice for personal identification. Like fingerprints and palmprints, footprints of individuals carry uniqueness; hence can be used in biometrics for personal recognition. This work investigates the powerfulness of footprints by extracting texture and shape features using Principal Component Analysis (PCA) method based upon Eigenfeet and introduces a new distance metric during the matching phase. Experimental results show that the new distance metric shows better results in comparison to the Euclidean, Manhattan and Mahalanobis distances.

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Personal Identification Technique based on Human Iris Recognition with Wavelet Transform

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. ◽

10.1109/icassp.2005.1415563 ◽

2006 ◽

Cited By ~ 3

Author(s):

Wen-Shiung Chen ◽

Kun-Huei Chih ◽

Sheng-Wen Shih ◽

Chih-Ming Hsieh

Keyword(s):

Wavelet Transform ◽

Iris Recognition ◽

Personal Identification ◽

Human Iris ◽

Identification Technique

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CNN-Based Personal Identification System Using Resting State Electroencephalography

Computational Intelligence and Neuroscience ◽

10.1155/2021/1160454 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Yongdong Fan ◽

Xiaoyu Shi ◽

Qiong Li

Keyword(s):

Resting State ◽

Data Augmentation ◽

Low Cost ◽

Personal Identification ◽

Acquisition Time ◽

Identification System ◽

Short Acquisition Time ◽

Average Accuracy ◽

Biometric Characteristic ◽

Identification Technique

As a biometric characteristic, electroencephalography (EEG) signals have the advantages of being hard to steal and easy to detect liveness, which attract researchers to study EEG-based personal identification technique. Among different EEG protocols, resting state signals are the most practical option since it is more convenient to operate than the other protocols. In this paper, a personal identification system based on resting state EEG is proposed, in which data augmentation and convolutional neural network are combined. The cross-validation is performed on a public database of 109 subjects. The experimental results show that when only 14 EEG channels and 0.5 seconds data are employed, the average accuracy and average equal error rate of the system can reach 99.32% and 0.18%, respectively. Compared with some existing representative works, the proposed system has the advantages of short acquisition time, low computational complexity, and rapid deployment using market available low-cost EEG sensors, which further advances the implementation of practical EEG-based identification systems.

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