Combination of gait multiple features at matching score level

Infrared (IR) Face recognition has become an area of growing interest since it can operate in dim light and total darkness. This paper introduces a comparative study between the infrared face recognition systems using the principle component analysis (PCA) and the Zernike moments (ZM) techniques. The performance is evaluated according to the recognition rate, time consumption, and immunity to both Salt & Pepper and Gaussian noise. The analysis shows that the PCA technique has the same performance as the ZM technique if a large size dataset is used. On the other hand, the ZM technique outperforms the PCA technique when using a small size dataset, but it consumes a time approximately equal to four times that required by the PCA technique. The simulations also show that the ZM technique outperforms the PCA technique in the presence of both Salt & Pepper and Gaussian Noise types at various values of noise variance.

This paper presents a combination techniques of appearance-based and feature-based feature extraction on the T-Zone face area to improve the recognition performance. This study shows that the T-Zone area and the combined technique provides a significant impact on the face recognition rate. A T-Zone face image is first divided into small regions where Local Binary Pattern (LBP) histograms are extracted and then concatenated into a single feature vector. This feature vector will further reduce the dimensionality scope by using the well established Principle Component Analysis (PCA) technique. Experiments have been carried out on the different sets of the Olivetti Research Laboratory (ORL) database. High recognition rates are obtained when compared to other face recognition methods of the same class. Our result shows of 7% improvement compared with PCA and 2% improvement compare with Sub-Holistic PCA. Our studies proves that the T-Zone area which is consisting of eyes and nose region is a ‘significant facial region’, and we also show that LBP can easily be combined with PCA to reduce the length of the feature vector, while the recognition performance is improved.

Reservoir history matching by ensemble smoother with principle component and sensitivity analysis for heterogeneous formations

Objective To discuss the predictive value of multiple ultrasonic features combination in diagnosis of thyroid papillary carcinoma (PTC) more than 1.0 cm in diameter. Methods The ultrasonic features of 258 PTC nodules from 251 patients and 207 nodular goiter (NG) nodules from 190 patients in the First People′s Hospital of Hangzhou were retrospectively analyzed. All the nodules were confirmed by pathological examination after surgery. The ultrasonic features included the shape of nodules, internal echo, anteroposterior/transverse diameter ratio (A/T), and microcalcification. The χ2 test was used to analyze the differences of ultrasonic features between PTC and NG. Multi-variate analyses (Logistic regression) was used to analyze the predictive risk ultrasonic features of PTC. The sensitivity and specificity of ultrasonic features were analyzed based on the gold standard of pathological results. Results There were significantly differences between 258 PTC nodules and 207 NG nodules in irregular shape, hypoechogenicity, A/T>1 and microcalcification (χ2 values were 121.511, 105.411, 41.483, 121.072, all P 1 and microcalcification were risk ultrasonic features of PTC. And their OR values were 5.013 (95%CI 2.919-8.610), 5.811 (95%CI 3.411-9.901), 15.399 (95%CI 7.576-31.301), 4.141 (95%CI 1.687-10.164) respectively. The sensitivity and specificity of single ultrasonic feature were 26.0%-79.5% and 71.5%-96.1%; the sensitivity and specificity of two ultrasonographic features combination were 11.2%-57.0% and 92.3%-99.0%; and the sensitivity and specificity of three or four ultrasonographic features combination were 8.1%-31.8% and 99.0%-99.5%. Conclusions Irregular shape, hypoechogenicity, A/T>1 and microcalcification of thyroid neoplasm are important ultrasonic features of PTC. Although the sensitivity of single ultrasonic feature in diagnosing PTC is higher than that of multiple features combination, it has a lower specificity. Therefore, combination of multiple ultrasonographic features can improve the specificity in diagnosing PTC and reduce the misdiagnosis of PTC. Key words: Ultrasonography; Thyroid neoplasm; Nodular goiter

Multibiometric systems fuse the evidence presented by different biometric sources in order to improve the matching accuracy of a biometric system. In such systems, information fusion can be performed at different levels; however, integration at the matching score level is the most commonly used approach due to the tradeoff between information content and accessibility. This work develops a tool in order to analyze the impact of various normalization schemes on the matching performance of score-level fusion algorithms. The tool permits the systematic evaluation of different fusion rules after employing normalizing and mapping the match scores of different modalities into a common domain. Furthermore, it provides a method to fit various parametric models to the score distribution and analyze the goodness of fit statistic based on the Chi-Squared and Kolmogorov-Smirnov tests. Experimental results on multiple datasets indicate the benefits of normalization, the role of parametric distributions and the variations in matching performance on different databases.

The principle component analysis(PCA) is a common feature extraction method in machine learning and pattern recognition approaches. PCA has been used in many applications, and face recognition in which specific faces are recognizing in an images database is one of the popular applications. The default distance metric which has been used with PCA based-face recognition is Euclidean distance. In this study, we have tested the Mahalanobis distance instead of Euclidean, and PCA based on Mahalanobis distance suggested a better performance on our students images database with highest recognition rate. However, we proposed weighted and normalized Mahalanobis distance based PCA-face recognition(PCA_WNMD). The proposed algorithm (PCA_WNMD) showed an improvement in faces recognition rate when tested on our students images database compared to PCA based on Mahalanobis and default Euclidean distances.

Automated human identification is a significant issue in real and virtual societies. Iris is a suitable choice for meeting this goal. In this paper, we present an iris recognition system that uses images acquired in both near-infrared and visible lights. These two types of images reveal different textural information of the iris tissue. We demonstrated the necessity to process both VL and NIR images to recognize irides. The proposed system exploits two feature extraction algorithms: one is based on 1D log-Gabor wavelet which gives a detailed representation of the iris region and the other is based on 1D Haar wavelet which represents a coarse model of iris. The Haar wavelet algorithm is proposed in this paper. It makes smaller iris templates than the 1D log-Gabor approach and yet achieves an appropriate recognition rate. We performed the fusion at the match score level and examined the performance of the system in both verification and identification modes. UTIRIS database was used to evaluate the method. The results were compared with other approaches and proved to have better recognition accuracy, while no image enhancement technique is utilized prior to the feature extraction stage. Furthermore, we demonstrated that fusion can compensate the lack of input image information, which can be beneficial in reducing the computation complexity and handling non-cooperative iris images.

Multiple Classifier System for Offline Malayalam Character Recognition

Multiple Classifier System (MCS) has attracted increasing interest in the field of pattern recognition and machine learning where this technique has also been introduced in remote sensing. The importance of classifier diversity in MCS has been raised recently; nevertheless, only a few of the researches have been studied in land cover classification problem. In this paper, a SPOT IV satellite image covering the Hong Kong Island and Kowloon Peninsula with six land cover classes were classified with four base classifiers: Minimum Distance Classifier, Maximum Likelihood Classifier, Mahalanobis Classifier and K-Nearest Neighbor Classifier. Same training and testing data sets were applied throughout the experiments and five Bayesian decision rules, including product rule, sum rule, max rule, min rule, and median rule, were utilized to construct different ensemble of classifiers. Performance of MCS was measured using the overall accuracy and kappa statistics, and three statistical tests including McNemarpsilas test, Cochranpsilas Q test and F-test were introduced to examine the dependence of the classification results. The experimental comparison reveals that (i) increasing the number of base classifiers may not improve the overall accuracy in MCS, (ii) significant diversity in base classifiers cannot enhance the overall performance and vice versa. These findings are noted with the condition in using the same data set and the same training set.

A hybrid method for MRI brain image classification

In multimodal biometric information fusion, it is common to encounter missing modalities in which matching cannot be performed. As a result, at the match score level, this implies that scores will be missing. We address the multimodal fusion problem involving missing modalities (scores) using support vector machines (SVMs) with the neutral point substitution (NPS) method. The approach starts by processing each modality using a kernel. When a modality is missing, at the kernel level, the missing modality is substituted by one that is unbiased with regards to the classification, called a neutral point. Critically, unlike conventional missing-data substitution methods, explicit calculation of neutral points may be omitted by virtue of their implicit incorporation within the SVM training framework. Experiments based on the publicly available Biosecure DS2 multimodal (scores) data set show that the SVM-NPS approach achieves very good generalization performance compared to the sum rule fusion, especially with severe missing modalities.

Identification and elucidation of anthropogenic source contribution in PM10 pollutant: Insight gain from dispersion and receptor models

In this paper, we present an approach of detecting speech presence for which the decision rule is based on a combination of multiple features using a sigmoid function. A minimum classification error (MCE) training is used to update the weights adjustment for the combination. The features, consisting of three parameters: the ratio of ZCR, the spectral energy, and spectral entropy, are combined linearly with weights derived from the sub-band domain. First, the Bark-scale wavelet decomposition (BSWD) is used to split the input speech into 24 critical sub-bands. Next, the feature parameters are derived from the selected frequency sub-band to form robust voice feature parameters. In order to discard the seriously corrupted frequency sub-band, a strategy of adaptive frequency sub-band extraction (AFSE) dependant on the sub-band SNR is then applied to only the frequency sub-band used. Finally, these three feature parameters, which only consider the useful sub-band, are combined through a sigmoid type function incorporating optimal weights based on MSE training to detect either a speech present frame or a speech absent frame. Experimental results show that the performance of the proposed algorithm is superior to the standard methods such as G.729B and AMR2.

In a biometric system, feature extraction is an important task for faster and efficient identification of a person. A new feature extraction method, sub-band PCA+LDA is proposed to extract distinct features from low frequency and high frequency wavelet sub-bands. The proposed method captures both local and global features of two biometrics under consideration, face and iris. The matching scores of face and iris are normalised using minmax and tanh techniques, and fused using sum rule, product rule and weighted sum rule. For unimodal systems, the proposed method gives better recognition rate in comparison to other existing methods, like DWT, DWT+PCA, DWT+LDA, local binary pattern and subspace LDA. The performance of the proposed multimodal biometric system is superior to unimodal system in terms of attaining maximum of 100% recognition rate and minimum equal error rate (EER) of 0.017 for standard biometric databases.

A spoof resistant multibiometric system based on the physiological and behavioral characteristics of fingerprint