scholarly journals Forgery Detection in Dynamic Signature Verification by Entailing Principal Component Analysis

2007 ◽  
Vol 2007 ◽  
pp. 1-8 ◽  
Author(s):  
Shohel Sayeed ◽  
S. Andrews ◽  
Rosli Besar ◽  
Loo Chu Kiong
Keyword(s):  
Principal Component Analysis ◽  
Detection System ◽  
Principal Component ◽  
Component Analysis ◽  
Error Rates ◽  
Forgery Detection ◽  
Data Glove ◽  
And Performance ◽  
Dynamic Signature ◽  
Operational Data

The critical analysis of the data glove-based signature identification and forgery detection system emphasizes the essentiality of noise-free signals for input. Lucid inputs are expected for the accuracy enhancement and performance. The raw signals that are captured using 14- and 5-electrode data gloves for this purpose have a noisy and voluminous nature. Reduction of electrodes may reduce the volume but it may also reduce the efficiency of the system. The principal component analysis (PCA) technique has been used for this purpose to condense the volume and enrich the operational data by noise reduction without affecting the efficiency. The advantage of increased discernment in between the original and forged signatures using 14-electrode glove over 5-electrode glove has been discussed here and proved by experiments with many subjects. Calculation of the sum of mean squares of Euclidean distance has been used to project the advantage of our proposed method. 3.1% and 7.5% of equal error rates for 14 and 5 channels further reiterate the effectiveness of this technique.

Shape of Western Australian dragon lizards (Agamidae)

2005 ◽  
Vol 26 (1) ◽  
pp. 73-85 ◽  
Author(s):  
Philip Withers ◽  
Graham Thompson
Keyword(s):  
Principal Component Analysis ◽  
Body Length ◽  
Size Effects ◽  
Principal Component ◽  
Component Analysis ◽  
Regression Residuals ◽  
Performance Traits ◽  
And Performance ◽  
Western Australian

AbstractFor 41 species of Western Australian agamid lizards, we found that most appendage lengths vary isometrically, so shape is largely independent of size. Of the three methods we used to quantitatively remove the effects of size on shape, the two that use principal component analysis (PCA; Jolicoeur, 1963; Somers, 1986; 1989) provided similar results, whereas regression residuals (against body length) provided a different interpretation. Somers' size-free PCA approach to remove the size-effects was the most useful because it provided 'size-free' scores for each species that were further analysed using other techniques, and its results seemed more biologically meaningful. Some, but not all, of the variation in size-free shape for these lizards could be related to phylogeny, retreat choice and performance traits.

Principal component analysis for the early detection of mastitis and lameness in dairy cows

2013 ◽  
Vol 80 (3) ◽  
pp. 335-343 ◽  
Author(s):  
Bettina Miekley ◽  
Imke Traulsen ◽  
Joachim Krieter
Keyword(s):  
Principal Component Analysis ◽  
Early Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Error Rates ◽  
Sensor Data ◽  
Sequence Length ◽  
Feeding Patterns ◽  
Original Dataset ◽  
Block Sensitivity

This investigation analysed the applicability of principal component analysis (PCA), a latent variable method, for the early detection of mastitis and lameness. Data used were recorded on the Karkendamm dairy research farm between August 2008 and December 2010. For mastitis and lameness detection, data of 338 and 315 cows in their first 200 d in milk were analysed, respectively. Mastitis as well as lameness were specified according to veterinary treatments. Diseases were defined as disease blocks. The different definitions used (two for mastitis, three for lameness) varied solely in the sequence length of the blocks. Only the days before the treatment were included in the blocks. Milk electrical conductivity, milk yield and feeding patterns (feed intake, number of feeding visits and time at the trough) were used for recognition of mastitis. Pedometer activity and feeding patterns were utilised for lameness detection. To develop and verify the PCA model, the mastitis and the lameness datasets were divided into training and test datasets. PCA extracted uncorrelated principle components (PC) by linear transformations of the raw data so that the first few PCs captured most of the variations in the original dataset. For process monitoring and disease detection, these resulting PCs were applied to the Hotelling's T2 chart and to the residual control chart. The results show that block sensitivity of mastitis detection ranged from 77·4 to 83·3%, whilst specificity was around 76·7%. The error rates were around 98·9%. For lameness detection, the block sensitivity ranged from 73·8 to 87·8% while the obtained specificities were between 54·8 and 61·9%. The error rates varied from 87·8 to 89·2%. In conclusion, PCA seems to be not yet transferable into practical usage. Results could probably be improved if different traits and more informative sensor data are included in the analysis.

scholarly journals Improved Approach for Identification of Real and Fake Smile using Chaos Theory and Principal Component Analysis

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Hayder Ansaf ◽  
Hayder Najm ◽  
Jasim Mohammed Atiyah ◽  
Oday A. Hassen
Keyword(s):  
Principal Component Analysis ◽  
Chaos Theory ◽  
Principal Component ◽  
Component Analysis ◽  
Human Beings ◽  
Multiple Parameters ◽  
Detection Approach ◽  
The Face ◽  
And Performance ◽  
Smile Detection

The smile detection approach is quite prominent with the face detection and thereby the enormous implementations are prevalent so that the higher degree of accuracy can be achieved. The face smile detection is widely associated to have the forensic of faces of human beings so that the future predictions can be done. In chaos theory, the main strategy is to have the cavernous analytics on the single change and then to predict the actual faces in the analysis. In addition, the integration of Principal Component Analysis (PCA) is integrated to have the predictions with more accuracy. This work proposes to use the analytics on the parallel integration of PCA and chaos theory to enable the face smile and fake identifications to be made possible. The projected work is analyzed using assorted parameters and it has been found that the deep learning integration approach for chaos and PCA is quite important and performance aware in the multiple parameters with the different datasets in evaluations.

scholarly journals Using Principal Component Analysis to Solve a Class Imbalance Problem in Traffic Incident Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Changjiang Zheng ◽  
Shuyan Chen ◽  
Wei Wang ◽  
Jian Lu
Keyword(s):  
Principal Component Analysis ◽  
Detection System ◽  
Class Imbalance ◽  
Principal Component ◽  
Component Analysis ◽  
Incident Detection ◽  
Least Squares Regression ◽  
Class Imbalance Problem ◽  
Significance Level ◽  
Traffic Incident

High imbalances occur in real-world situations when a detection system needs to identify the rare but important event of a traffic incident. Traffic incident detection can be treated as a task of learning classifiers from imbalanced or skewed datasets. Using principal component analysis (PCA), a one-class classifier for incident detection is constructed from the major and minor principal components of normal instances. Experiments are conducted with a real traffic dataset collected from the A12 highway in The Netherlands. The parameters setting, including the significance level, the percentage of the total variation explained, and the upper bound of the eigenvalues for the minor components, is discussed. The test results demonstrate that this method achieves better performance than partial least squares regression. The method is shown to be promising for traffic incident detection.

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