International Journal of Biometrics (23 papers in press)
A Spatial Pyramidal Decomposition Method for Finger Vein Recognition Using Local Descriptors
by Badreddine Griouz, Rafik Djemili, Hocine Bourouba, Hakim Doghmane
Abstract: Finger vein patterns have been proved as one of the most promising biometric modality for its convenience and security. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. This manner of processing however may not provide optimal recognition accuracies as reported in many studies. Therefore, this paper proposes in the feature extraction stage, the use of the spatial pyramid decomposition (SPD) method aiming at partitioning the finger vein images into increasingly fine sub-regions from which local texture descriptors are obtained. The descriptors adopted in this paper are local binary pattern (LPB), binarized statistical image feature (BSIF) and local phase quantization (LPQ). The performance of the proposed approach evaluated on two publicly databases PolyU and SDUMLA achieves a recognition accuracy higher than that of some existing systems reported in the literature for both the SDUMLA and the PolyU databases.
Keywords: Finger Vein Recognition; Spatial Pyramid Decomposition; LBP; BSIF; LPQ.
Multi-pose facial expression recognition using Rectangular HOG feature extractor and Label-Consistent KSVD classifier
by Ali Muhamed Ali, Hanqi Zhuang, Ali Ibrahim
Abstract: In this paper, a new approach to the classification of facial expressions from multiple pose images is proposed. In this approach, a Rectangular Histogram of Oriented Gradient (R-HOG) algorithm is first designed to extract features of face images. The parameters of the R-HOG algorithm, which is a modification of the original HOG algorithm include cell shape, cell size, block size, and the number of orientation bins. The R-HOG is capable of capturing more discriminative texture features of different facial expressions. In addition, a supervised dictionary learning classifier, the Label Consistent K-SVD (LC-KSVD) algorithm, is adopted to recognize the facial expression of the subject.
To investigate its effectiveness, the proposed technique was applied to classify emotional states of the face images in the two public available facial expression datasets: KDFE and RafD. The experiment study showed that the new method outperformed in many aspects those methods reported in the literature tested with the same datasets. First, the new method handles pose variations better. Second, it is more robust in cases where the size of a training dataset is small. Finally, it's accuracy performance is more consistent measured by standard deviations.
Keywords: Facial Expression Recognition; Emotional Classification; Sparse Coding; Dictionary Learning; Histogram Oriented Gradient; Label-Consistent KSVD.
An Accurate and Fast Method for Eyelid Detection
by Ahmed A.K. Tahir, Steluta Anghelus
Abstract: A novel method called Refine-Connect-Extend-Smooth (R-C-E-S) for detecting eyelids is presented. It consists of four algorithms, Canny edge detector with Prewitt operator, Modified Refine Edge Map (MREM), Connect Edges-Extend (CEE) and Smooth Curve (SC). The method is not based on pre-assumptions that consider eyelids as parabola or lines and it does not use curve fitting algorithm, therefore sever deviation of the detected eyelid curve from the actual eyelid path is avoided. The method is applied to three types of database, CASIA-V1.0, CASIA-V4.0Lamp and SDUMLA-HMT. For CASIA-V1.0 the accuracies are 93.2%, 99.1% and 96.7% for detecting lower eyelid, upper eyelid and free iris and the processing times are 42 ms, 49 ms and 35 ms. For CASIA-V4.0-Lamp these accuracies are 97.6%, 98.3% and 97.8% with processing time 23 ms, 26 ms and 21 ms. For SDUMLA-HMT the accuracies are 95.1%, 95.3% and 96.92% with processing time 35 ms, 40 ms and 31 ms.
Keywords: Biometics; Canny Edge Detector; Eyelid Detection; Iris Localization; Iris Recognition System; Prewitt Operator; Sobel Operator.
New method for identification of persons using geometry foot outline
by Khadidja Kafi, Adda Ali Pacha, Naima Hadj Said
Abstract: In recent years, identification systems with using biometric features are receiving considerable attention. Iris, palmprint, and footprint are shown as examples. The present study is an attempt to evaluate uniqueness of foot and its use at a possible means of identification using foot-biometric features in face based on foot outline. In order to study this uniqueness, a computer database has been constructed taking 19 right foot outline measurements in centimeters from feet pictures of 102 volunteers(85male and 17 female) using the measuring tool in photofilte. Using the exact measurements and these measurements with
Keywords: Biometrics; foot outline measurements; personal identification.
Biometric Face Classification with the Hybridized Rough Neural Network
by Sasirekha Kathirvel, Thangavel K
Abstract: Face biometric plays a vital role to authenticate a person in a right way. Face classification is an important indexing scheme to reduce face matching time for large volumes of a database. In this paper, a hybridized approach based on Rough Set Theory (RST) and Back Propagation Neural Network (BPN) for gender classification using human face images is proposed. It involves four main parts: Pre-processing, Feature Extraction, Feature Selection and Classification. Initially, the images are converted to grayscale and then the median filter is applied to de-noise. The features have been extracted using Local Binary Pattern (LBP) method as they exploit the rich discriminatory information existing in the face images. The evolutionary optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), hybridization of ACO and GA (ACO-GA) and hybridization of PSO and GA (PSO-GA) are investigated for feature selection from the face. Finally, the hybridized Rough Neural Network (RNN) is employed to classify the face images. In this research, experiments have been conducted on real-time face images collected from 155 subjects each with ten orientations using Logitech WebCam and also on ORL face dataset. The experimental result of the proposed RNN is compared in terms of precision, recall, f-measure, accuracy and error rate with other benchmark classification techniques such as Naive Bayes, Support Vector Machine (SVM), Radial Basis Function Network (RBFN) conventional BPN, and Convolutional Neural Network (CNN) to conclude the efficacy of the proposed approach.
Keywords: ACO; Biometric Face; GA; Gender; PSO; Rough Neural Network.
An Improved Weber Face Based Method For Face Recognition Under Uncontrolled Illumination Conditions
by Boualleg AbdelHalim, Deriche Mohamed, Sedraoui Moussa
Abstract: This paper presents a new face recognition system robust to illumination variations and moderate occlusion. Two main contributions are discussed. First, we introduce an approach based on Contrast Equalization (CE) to improve the traditional Weberface (WF) technique and make it more robust. Second, we use the Local Binary Patterns (LBP) and Local Phase Quantization (LPQ) descriptors to make the Weberface method more resilient to extreme variations in illumination by exploiting both spatial-domain and frequency-domain information. Finally, by combining the outputs of the two descriptors, enhanced facial features are obtained which are shown to possess more discriminating power not only for variable lighting conditions but also for occlusion. Once the features are extracted, these are used with a simple Knearest neighbor classifier. The concept of using the (WF) model together with spatial and frequency domains descriptors is novel and proven to result in a robust system resilient to varying lighting conditions and small to moderate variations in pose, and moderate occlusion. The effectiveness of the method is validated and compared to many classical illumination compensation techniques over three public datasets; namely the Yale B, the extended Yale B, and the AR databases. The proposed algorithm is shown to consistently outperform existing techniques under different challenging environments.
Keywords: face recognition; illumination normalization; local texture patterns; contrast enhancement; pattern classification.
Geometric retrieval algorithm based ear biometry with occluded images
by Samik Chakraborty, Madhuchhanda Mitra, Saurabh Pal
Abstract: Ear is a potential biometric parameter which has drawn the attention due to its structural uniqueness and stability over the age, obesity, disease, expression etc. unlike other common biometric traits. In this work a geometric retrieval algorithm has been proposed for ear based biometric analysis with occluded image. First the occlusion problem is countered by an empirical data driven technique and then PSO based optimal features are extracted for comparison that reveals the authenticity of the subject with respect to a stored database. A search of minima from Euclidian distance based analysis is used for final decision. The proposed system is tested on 50 subjects collected in multiple sessions in laboratory with a good recognition rate superior to similar reported works as indicated in the result section.
Keywords: biometry; ear recognition; occlusion; interpolation; particle swarm optimization; Euclidian distance.
Eigen-based Binary Feature Amalgamation in Multimodal Biometrics
by Wen-Shiung Chen, Ren-He Jeng
Abstract: In this paper, a quantized eigen analysis (QEA) for the extracted features is proposed and rnan associated eigen-based binary feature amalgamation (EBFA) based on QEA is developed rnfor feature fusion in multimodal biometrics. rnAs opposed to feature combination, EBFA projects heterogeneous features onto the projection kernel and rnuses only the sign parts to encode the features as bit strings to maximize its expression rnrather than directly combine them. rnThus the feature codes can be simply concatenated or compared by XOR bit-wise operation rninto a serial or parallel amalgamated feature vector. rnTo evaluate the performance of EBFA, a series of experiments are performed on rnmultiple biometric modalities, including face, palm-print and iris. rnThe experimental results show that the proposed binary feature amalgamation scheme at feature-level rnis superior to some other feature fusion methods and score-level methods in terms of multimodal recognition accuracy performance.
Keywords: Multimodal Biometrics; Feature-Level Fusion; Feature Combination; Feature Fusion; rnFeature Amalgamation; Eigen Analysis; Face; Iris; Palm-print.
LAUGHTER SIGNATURE: A NOVEL BIOMETRIC TRAIT FOR PERSON IDENTIFICATION
by Comfort Folorunso, Olumuyiwa Asaolu, Oluwatoyin Popoola
Abstract: Laughter is a naturally occurring feature in speech and social interactions. Human intelligence can identify people by their laughter, but this has not been explored as a potential biometric in person identification systems. This study proposes a novel behavioral biometric based on individual laughter signatures. Mel Frequency Cepstral Coefficients (MFCC) features were extracted and Kruskal-Wallis test was performed on each coefficient. A Dynamic-Average Mel Frequency Cepstral Coefficients (DA-MFCC) was developed from the typical MFCC features for system training using Gaussian Mixture Model (GMM) and Support Vector Machine (SVM). Test results showed an accuracy of 90%-person identification for SVM while the GMM was 65%. The use of GA-MFCC improved the accuracy of the system by 5.06% and 2.99% for GMM and SVM respectively. Laughter has thus been shown to be a viable biometric feature for person identification which can be embedded into artificial intelligence systems in diverse applications.
Keywords: Person identification; laughter signature; biometrics trait; Support Vector Machine (SVM); Gaussian mixture Model(GMM).
Efficient Fusion of Face and Palmprint in Gabor Filtered Wigner Domain
by Nirmala Saini, Aloka Sinha
Abstract: In this paper, a new transform Gabor Filtered Wigner transform (GFWT) has been proposed. In GFWT, Gabor filtering is performed on the Wigner transformed image. Wigner transform gives a simultaneous representation of an image in time and frequency domain which is further processed using Gabor filters. The proposed transform is then used to extract the features from the biometrics to develop different multimodal biometric systems. A detailed study has been carried out in which, different unimodal and multimodal systems such as feature level and score level fusion are analysed. In order to improve the performance of the system, an optimization technique, particle swarm optimization (PSO) is used to find the optimal parameters of the Gabor filter and to select the significant GFWT feature vector. The PSO technique not only improves the performance of the system but also able to reduce the dimension of the feature vectors. Numerical experiments are carried out on face and palmprint database to show the effectiveness of the proposed transform for different unimodal and multimodal systems.
Keywords: Multimodal system; Feature level fusion; Score level fusion; Gabor filtered Wigner transform; Particle swarm optimization.
Fingerprint pores extraction by using automatic scale selection
by Diwakar Agarwal, Atul Bansal
Abstract: Extraction of fingerprint sweat pores is a critical step in those applications which are based on highly secured features. Pores are varying in scale (size) and evenly distributed along the ridges. It is the main challenge to design a technique which determines the pores of different sizes in the fingerprint image. In this paper, pore extraction algorithm is proposed for high-resolution fingerprint images which utilized multiscale ?-normalized Laplacian of Gaussian (LoG) filter. A block-wise approach is implemented in which each region is filtered at multiple scale values. Scale space theory is applied and candidate pixels of high negative response are identified through local maxima approach. The efficacy of the proposed algorithm is tested by measuring average True Detection Rate (TDR) and average False Detection Rate (FDR). Results of the proposed algorithm achieve TDR and FDR values as 82.89% and 21.2% respectively which are better in comparison to the state-of-art techniques.
Keywords: automatic scale selection;biometrics;fingerprint;local maxima;pores.
Special Issue on: Biometrics Challenges and Applications
FACE DETECTION AND RECOGNITION SYSTEM BASED ON HYBRID STATISTICAL, MACHINE LEARNING AND NATURE BASED COMPUTING
by Vinodini Ramamurthy, Karnan M.
Abstract: Face detection becomes an important task carried out in biometric based security system and identification application. This paper presents the detailed investigations on different methods suffer from accuracy and computational complexity used for the face detection and recognition. The face detection and recognition with high performance ratio for face detection and recognition is achieved in the methods investigated. The reduction of complexity can happen at any stages of the face recognition like preprocessing, segmentation, feature extraction, recognition etc. The proposed method presented in this paper is based on PCA (principle component analysis), SVM (support vector machine), K-nearest neighbor (KNN) and ACO (ant colony optimization). The detail investigation of the proposed method is made and is compared with the existing methods. From the performance it can be observed that the proposed method is better in performance when compared to other methods.
Keywords: Face detection; recognition; PCA; SVM; ACO; segmentation; feature extraction; classification.
Special Issue on: Intelligent Computing for the Epidemic Challenges of Biometrics
Design of Embedded Image Teaching System Based on ARM Technology
by Wang Can
Abstract: With the continuous development of multimedia and speech teaching laboratory in Colleges and universities, and the application of digital processing technology, digital voice teaching equipment is attracting more and more attention from domestic universities and instrument manufacturers.Based on the core technology of ARM and DSP dual core technology, the theoretical analysis of digital language learning system is carried out, and the student terminal circuit and teaching software system are designed in detail, and the function and technical index of the whole system are tested.The test results show that the digital speech learning system constructed by the student terminal controlled by ARM and DSP binuclear is fully satisfied with the actual language teaching requirements.
Keywords: ARM technology; embedded image; teaching system.
Linearization Control of AC Permanent Magnet Synchronous Motor Servo System Based on Sensor Technology
by Liu Yongqiu
Abstract: AC permanent magnet synchronous motor servo control system is a complex nonlinear, strong coupling and time-varying system. It has strong uncertainty and nonlinearity, and when the system is running, it also will be disturbed to varying degrees, so the conventional control strategy is difficult to meet the control requirements of high accuracy, high speed and high-performance servo system. This paper adopts a direct feedback linearization control strategy based on sensor technology and uses w, i_das the output of the system to realize the decoupling of the system. In addition, the grey prediction is added to overcome the shortcomings of direct feedback linearization that is sensitive to parameters. Adjusting the uncertain factors block by grey prediction to adapt to the direct feedback linearization rule and achieve the desired effect. MATLAB/Simulink is used to complete the simulation of servo control algorithm. The simulation results show that the direct feedback linearization control is better than the conventional PID control, and the direct feedback linearization control algorithm with grey prediction can improve the performance of the permanent magnet synchronous motor servo control system and can meet the basic requirements of the high-performance servo control system.
Keywords: Permanent magnet synchronous motor; Direct feedback linearization; Linearization control; MATLAB simulation; Sensor.
Research on Optimization of Complex Model of Large-Scale Building Structure Dependent on Adaptive Grey Genetic Algorithm
by Shi Xiaohong
Abstract: Genetic algorithm (GA) is a bionics algorithm based on the biological evolution theory that has received extensive attention in the field of computer science and Optimization in recent years. This paper analyzes and integrates the relevant contents of genetic algorithm and its application in the optimal design of large-scale building structures and analyzes and researches briefly several key factors when the genetic algorithm is applied to the optimal design of large-scale building structures, such as mathematical modeling, constraint condition treatment, generation of initial population and selection of control parameters of genetic algorithm. However, because the simple genetic algorithm is only good at global search, and the local search ability is not enough, it will take quite a long time to achieve the real optimal solution. For the shortcomings of simple genetic algorithm, an improved adaptive grey genetic algorithm is proposed in this paper. The example shows that the obtained adaptive genetic algorithm can improve the convergence and calculation speed when the genetic algorithms is applied to structural optimization design.
Keywords: Large-scale buildings; Structural optimization; Adaptive; Grey genetic algorithm.
Research on Evaluation Model of Deep Foundation Pit Supporting structure in Urban Traffic Tunnel
by He Wenbiao
Abstract: In order to improve the building mechanics performance of the foundation pit supporting structure of urban traffic tunnel and guide the engineering design and construction, the evaluation model of the deep foundation pit supporting structure of urban traffic tunnel is proposed based on mechanical anchoring and linear mechanical loading. The mechanical anchoring method is used to load the support structure of deep foundation pit of urban traffic tunnel. The elastic modulus and yield strength of the support structure of deep foundation pit of urban traffic tunnel are taken as the constraint parameters. The dynamic model of supporting structure in deep foundation pit of tunnel is established. The stiffness degradation increment of urban traffic tunnel deep foundation pit support structure is analyzed by SCBRB component. Under the distribution of bilinear elastic residual stress, the deformation increment of urban traffic tunnel deep foundation pit support structure is obtained. The constitutive relation of dynamic node of deep foundation pit supporting structure of urban traffic tunnel under moving load is constructed, and the tangent elastic modulus of supporting structure of deep foundation pit of tunnel is calculated according to equivalent stress-strain relation. Thus, the mechanical evaluation and optimization design of deep foundation pit supporting structure of urban traffic tunnel are realized. The test results show that the mechanical loading performance of the model is good for the design of the deep foundation pit supporting structure of urban traffic tunnel, and the load capacity of the supporting structure is improved, and the engineering design of the foundation pit supporting structure is optimized.
Keywords: urban traffic tunnel; supporting structure; load; mechanics; evaluation model.
Research on Educational informatization Platform Based on Web2.0
by Wang Zhixue
Abstract: Educational informatization is an important part of national informatization strategy. The realization of educational informatization plays a fundamental, global and lasting role in social development. The Ministry of education degree and graduate education research center takes the guarantee and improvement of graduate education quality as the core goal, proposes the idea of sharing and comprehensive analysis of degree data through the establishment of a unified platform, and establishes the management of educational information of degree and postgraduate students, service system planning and design subject. Under this background, this paper deeply studies the current situation of educational informationization development and the relevant technology of informationization platform and proposes the idea of introducing the informationization platform into the construction of higher education informatization platform. The advent of the Web2.0 era has injected new impetus into the development of network education. Web2.0 has the characteristic of personalization, decentralization, openness, interactivity, and sociality. It corresponds to the educational idea advocated by modern educational theory. Through the method of literature analysis, this paper collates and think about the current educational application research under the Web2.0, and finally through specific case analysis and comparison to verify the views and draw conclusions and provide reference for the future research of Web2.0 and educational informatization platform.
Keywords: Internet; Web2.0; Technology; Network education; Education platform;.
Research on Network Intrusion Detection Security Based on Improved Extreme Learning Algorithm and Neural Network algorithm
by Dai Zhenjun
Abstract: In order to improve the ability of network fuzzy intrusion detection, a network intrusion detection method based on improved extreme learning algorithm and neural network algorithm is proposed to improve the security of the network. ARMA and other linear detection methods are used to construct the network intrusion signal model, and the nonlinear time series and chaos analysis methods are used to extract the feature of network intrusion and big data information analysis. The limit learning method is used for active detection of network intrusion, the adaptive learning method is used for iterative analysis of network intrusion detection, and the correlation characteristic decomposition method is used to improve the convergence of network intrusion detection. The fuzzy neural network algorithm is used to classify the network intrusion features to improve the intrusion detection performance. The simulation results show that this method has high accuracy and strong anti-jamming ability, it has good application value in network security.
Keywords: extreme learning; network intrusion; neural network algorithm; detection; nonlinear time series analysis.
RESEARCH ON MANIPULATOR MOTION CONTROL BASED ON NEURAL NETWORK ALGORITHM
by Shi Qiongyan, Zhang Jianghua
Abstract: The manipulator is the new artificial intelligence device, and its motion control is the basis for ensuring the stability of the manipulator's attitude. The traditional manipulator motion control adopts the static neuron control method, which will lead to small disturbance in the attitude control of the manipulator, and cause the stable motion performance of the manipulator. A motion control algorithm for manipulator is proposed based on variable structure fuzzy PID neural network. The coordinate system structure description and manipulator dynamics analysis of the controlled system are carried out. The motion control algorithm of the manipulator is improved by using variable structure PID neural network control and adaptive disturbance suppression method. Combined with the strict feedback control method, the motion error of the manipulator is compensated, and the steady-state error is corrected by the adaptive inertial compensation method to realize the motion control optimization of the manipulator. The simulation results show that the motion control algorithm of the manipulator has better positioning performance and better control stability, reduces the steady-state error and improves the control stability.
Keywords: neural network; manipulator; fuzzy PID; neural network.
Research on Logistics Distribution path Analysis based on artificial Intelligence algorithm
by Yao Cuiping
Abstract: Logistics distribution path optimization model design is the key to ensure the smooth flow of logistics distribution path network, the logistics distribution path optimization is designed to improve the efficiency of logistics distribution, a logistics distribution path optimization model is proposed based on artificial intelligence algorithm. A logistics distribution path search model based on rough set theory is established. Ant colony search method is used to design the artificial intelligence algorithm of logistics distribution path optimization. Adaptive weighting method is used to extract and schedule the information of logistics distribution path, and the shortest path optimization method is used to optimize the route planning of logistics distribution, which can reduce the path overhead and time cost of logistics distribution. The efficiency of logistics distribution is improved. The simulation results show that this method is used to construct the logistics distribution path model, which reduces the time cost and the road cost of the logistics distribution, and improves the throughput of the logistics distribution significantly.
Keywords: artificial intelligence algorithm; logistics distribution; ant colony search; shortest path optimization.
Study on fatigue of bus drivers based on biological model
by Xiao Hong
Abstract: In order to improve the fatigue detection ability of public transport drivers, the biometric modeling method is used to test and evaluate the fatigue of drivers. A fatigue detection model for public transport drivers is proposed based on biological mathematical model analysis, and the prevention and evaluation according to the fatigue test model is constructed. According to the visual, neural and blood supply characteristics of public transport drivers, the mathematical model of quantitative recursive statistical analysis of public transport drivers' biological fatigue is established by using descriptive statistical analysis method. The problem of public transport driver testing is transformed into an optimal solution problem for a continuous time-delay non-smooth system. Under the condition of non-smooth autonomous continuous boundedness, a biologic mathematical model of fatigue detection is constructed. The delay-dependent sufficient conditions of public transport driver fatigue testing are obtained to prevent and monitor driving fatigue accurately. The test results show that the model is accurate for public traffic drivers' fatigue testing. According to the biometric test results, it can reliably reflect whether the driver is tired or not, so that the danger alarm can be carried out and the driving safety can be ensured.
Keywords: biological mathematical model; public transport driver; fatigue test; vision; nerve.
Research on optimal Design of Municipal roads based on genetic algorithm
by Li Shufang, Zhang Xiu, Li Zhang
Abstract: In order to improve the rationality of the design of municipal roads and realize the optimal planning and design of municipal roads, an empirical road optimization design method based on genetic algorithm is proposed. In the sample of remote sensing image of municipal road, by solving the zero uniform ergodic characteristic and logical difference variable scale characteristic of objective function, the analysis and design of complex urban road pattern is realized, according to the principle of pixel correlation, Genetic algorithm is used to optimize the design of municipal roads, and the image merging planning analysis model of municipal roads is constructed. The feature classification of municipal roads is carried out by genetic method, and the cross edge contour feature segmentation method of municipal roads is adopted. Optimize planning and network design of municipal roads. The simulation results show that this method can improve the rational layout of municipal road planning and improve the spatial planning capacity and traffic capacity of municipal roads.
Keywords: genetic algorithm; municipal road; image; feature segmentation; networking.
ALGORITHM RESEARCH OF SPOKEN ENGLISH ASSESSMENT BASED ON FUZZY MEASURE AND SPEECHRECOGNITION TECHNOLOGY
by Cao Dongbo, Guo Ying
Abstract: At present, many speech recognition algorithms are difficult to effectively evaluate the fuzziness of the evaluation algorithm . Based on this, this dissertation uses the speech recognition technology based on fuzzy measure to evaluate the spoken English. In the study the fuzzy measure, based on the traditional algorithm, is used to evaluate the spoken English and different characteristic parameters are extracted to construct the corresponding evaluation model. Simultaneously, the pronunciation is evaluated through automatic learning rules. The English speaking assessment model based on fuzzy measure and speech recognition technology is constructed and validated. The research shows that compared with the traditional algorithms, the spoken language evaluation algorithm based on fuzzy measure and speech recognition technology has the incomparable superiority, and can provide a reference for the follow-up related research.
Keywords: Fuzzy measure; Speech recognition; Spoken English; Evaluation algorithm.