Search results for: Target recognition
1347 A Modified Speech Enhancement Using Adaptive Gain Equalizer with Non linear Spectral Subtraction for Robust Speech Recognition
Authors: C. Ganesh Babu, P. T. Vanathi
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In this paper we present an enhanced noise reduction method for robust speech recognition using Adaptive Gain Equalizer with Non linear Spectral Subtraction. In Adaptive Gain Equalizer method (AGE), the input signal is divided into a number of subbands that are individually weighed in time domain, in accordance to the short time Signal-to-Noise Ratio (SNR) in each subband estimation at every time instant. Instead of focusing on suppression the noise on speech enhancement is focused. When analysis was done under various noise conditions for speech recognition, it was found that Adaptive Gain Equalizer method algorithm has an obvious failing point for a SNR of -5 dB, with inadequate levels of noise suppression for SNR less than this point. This work proposes the implementation of AGE when coupled with Non linear Spectral Subtraction (AGE-NSS) for robust speech recognition. The experimental result shows that out AGE-NSS performs the AGE when SNR drops below -5db level.
Keywords: Adaptive Gain Equalizer, Non Linear Spectral Subtraction, Speech Enhancement, and Speech Recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16931346 Myanmar Character Recognition Using Eight Direction Chain Code Frequency Features
Authors: Kyi Pyar Zaw, Zin Mar Kyu
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Character recognition is the process of converting a text image file into editable and searchable text file. Feature Extraction is the heart of any character recognition system. The character recognition rate may be low or high depending on the extracted features. In the proposed paper, 25 features for one character are used in character recognition. Basically, there are three steps of character recognition such as character segmentation, feature extraction and classification. In segmentation step, horizontal cropping method is used for line segmentation and vertical cropping method is used for character segmentation. In the Feature extraction step, features are extracted in two ways. The first way is that the 8 features are extracted from the entire input character using eight direction chain code frequency extraction. The second way is that the input character is divided into 16 blocks. For each block, although 8 feature values are obtained through eight-direction chain code frequency extraction method, we define the sum of these 8 feature values as a feature for one block. Therefore, 16 features are extracted from that 16 blocks in the second way. We use the number of holes feature to cluster the similar characters. We can recognize the almost Myanmar common characters with various font sizes by using these features. All these 25 features are used in both training part and testing part. In the classification step, the characters are classified by matching the all features of input character with already trained features of characters.
Keywords: Chain code frequency, character recognition, feature extraction, features matching, segmentation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7381345 A System of Automatic Speech Recognition based on the Technique of Temporal Retiming
Authors: Samir Abdelhamid, Noureddine Bouguechal
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We report in this paper the procedure of a system of automatic speech recognition based on techniques of the dynamic programming. The technique of temporal retiming is a technique used to synchronize between two forms to compare. We will see how this technique is adapted to the field of the automatic speech recognition. We will expose, in a first place, the theory of the function of retiming which is used to compare and to adjust an unknown form with a whole of forms of reference constituting the vocabulary of the application. Then we will give, in the second place, the various algorithms necessary to their implementation on machine. The algorithms which we will present were tested on part of the corpus of words in Arab language Arabdic-10 [4] and gave whole satisfaction. These algorithms are effective insofar as we apply them to the small ones or average vocabularies.Keywords: Continuous speech recognition, temporal retiming, phonetic decoding, algorithms, vocal signal, dynamic programming.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13381344 Video-Based Face Recognition Based On State-Space Model
Authors: Cheng-Chieh Chiang, Yi-Chia Chan, Greg C. Lee
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This paper proposes a video-based framework for face recognition to identify which faces appear in a video sequence. Our basic idea is like a tracking task - to track a selection of person candidates over time according to the observing visual features of face images in video frames. Hence, we employ the state-space model to formulate video-based face recognition by dividing this problem into two parts: the likelihood and the transition measures. The likelihood measure is to recognize whose face is currently being observed in video frames, for which two-dimensional linear discriminant analysis is employed. The transition measure estimates the probability of changing from an incorrect recognition at the previous stage to the correct person at the current stage. Moreover, extra nodes associated with head nodes are incorporated into our proposed state-space model. The experimental results are also provided to demonstrate the robustness and efficiency of our proposed approach.
Keywords: 2DLDA, face recognition, state-space model, likelihood measure, transition measure.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16751343 Capturing an Unknown Moving Target in Unknown Territory using Vision and Coordination
Authors: Kiran Ijaz, Umar Manzoor, Arshad Ali Shahid
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In this paper we present an extension to Vision Based LRTA* (VLRTA*) known as Vision Based Moving Target Search (VMTS) for capturing unknown moving target in unknown territory with randomly generated obstacles. Target position is unknown to the agents and they cannot predict its position using any probability method. Agents have omni directional vision but can see in one direction at some point in time. Agent-s vision will be blocked by the obstacles in the search space so agent can not see through the obstacles. Proposed algorithm is evaluated on large number of scenarios. Scenarios include grids of sizes from 10x10 to 100x100. Grids had obstacles randomly placed, occupying 0% to 50%, in increments of 10%, of the search space. Experiments used 2 to 9 agents for each randomly generated maze with same obstacle ratio. Observed results suggests that VMTS is effective in locate target time, solution quality and virtual target. In addition, VMTS becomes more efficient if the number of agents is increased with proportion to obstacle ratio.Keywords: Vision, MTS, Unknown Target, Coordination, VMTS, Multi-Agent.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14541342 On-line Recognition of Isolated Gestures of Flight Deck Officers (FDO)
Authors: Deniz T. Sodiri, Venkat V S S Sastry
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The paper presents an on-line recognition machine (RM) for continuous/isolated, dynamic and static gestures that arise in Flight Deck Officer (FDO) training. RM is based on generic pattern recognition framework. Gestures are represented as templates using summary statistics. The proposed recognition algorithm exploits temporal and spatial characteristics of gestures via dynamic programming and Markovian process. The algorithm predicts corresponding index of incremental input data in the templates in an on-line mode. Accumulated consistency in the sequence of prediction provides a similarity measurement (Score) between input data and the templates. The algorithm provides an intuitive mechanism for automatic detection of start/end frames of continuous gestures. In the present paper, we consider isolated gestures. The performance of RM is evaluated using four datasets - artificial (W TTest), hand motion (Yang) and FDO (tracker, vision-based ). RM achieves comparable results which are in agreement with other on-line and off-line algorithms such as hidden Markov model (HMM) and dynamic time warping (DTW). The proposed algorithm has the additional advantage of providing timely feedback for training purposes.Keywords: On-line Recognition Algorithm, IsolatedDynamic/Static Gesture Recognition, On-line Markovian/DynamicProgramming, Training in Virtual Environments.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13221341 A New Approach to ECG Biometric Systems: A Comparitive Study between LPC and WPD Systems
Authors: Justin Leo Cheang Loong, Khazaimatol S Subari, Rosli Besar, Muhammad Kamil Abdullah
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In this paper, a novel method for a biometric system based on the ECG signal is proposed, using spectral coefficients computed through linear predictive coding (LPC). ECG biometric systems have traditionally incorporated characteristics of fiducial points of the ECG signal as the feature set. These systems have been shown to contain loopholes and thus a non-fiducial system allows for tighter security. In the proposed system, incorporating non-fiducial features from the LPC spectrum produced a segment and subject recognition rate of 99.52% and 100% respectively. The recognition rates outperformed the biometric system that is based on the wavelet packet decomposition (WPD) algorithm in terms of recognition rates and computation time. This allows for LPC to be used in a practical ECG biometric system that requires fast, stringent and accurate recognition.
Keywords: biometric, ecg, linear predictive coding, wavelet packet decomposition
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 28821340 Search Engine Module in Voice Recognition Browser to Facilitate the Visually Impaired in Virtual Learning (MGSYS VISI-VL)
Authors: Nurulisma Ismail, Halimah Badioze Zaman
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Nowadays, web-based technologies influence in people-s daily life such as in education, business and others. Therefore, many web developers are too eager to develop their web applications with fully animation graphics and forgetting its accessibility to its users. Their purpose is to make their web applications look impressive. Thus, this paper would highlight on the usability and accessibility of a voice recognition browser as a tool to facilitate the visually impaired and blind learners in accessing virtual learning environment. More specifically, the objectives of the study are (i) to explore the challenges faced by the visually impaired learners in accessing virtual learning environment (ii) to determine the suitable guidelines for developing a voice recognition browser that is accessible to the visually impaired. Furthermore, this study was prepared based on an observation conducted with the Malaysian visually impaired learners. Finally, the result of this study would underline on the development of an accessible voice recognition browser for the visually impaired.Keywords: Accessibility, Usability, Virtual Learning, Visually Impaired, Voice Recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20291339 Support Vector Machine for Persian Font Recognition
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In this paper we examine the use of global texture analysis based approaches for the purpose of Persian font recognition in machine-printed document images. Most existing methods for font recognition make use of local typographical features and connected component analysis. However derivation of such features is not an easy task. Gabor filters are appropriate tools for texture analysis and are motivated by human visual system. Here we consider document images as textures and use Gabor filter responses for identifying the fonts. The method is content independent and involves no local feature analysis. Two different classifiers Weighted Euclidean Distance and SVM are used for the purpose of classification. Experiments on seven different type faces and four font styles show average accuracy of 85% with WED and 82% with SVM classifier over typefacesKeywords: Persian font recognition, support vector machine, gabor filter.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16961338 Voice Driven Applications in Non-stationary and Chaotic Environment
Authors: C. Kwan, X. Li, D. Lao, Y. Deng, Z. Ren, B. Raj, R. Singh, R. Stern
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Automated operations based on voice commands will become more and more important in many applications, including robotics, maintenance operations, etc. However, voice command recognition rates drop quite a lot under non-stationary and chaotic noise environments. In this paper, we tried to significantly improve the speech recognition rates under non-stationary noise environments. First, 298 Navy acronyms have been selected for automatic speech recognition. Data sets were collected under 4 types of noisy environments: factory, buccaneer jet, babble noise in a canteen, and destroyer. Within each noisy environment, 4 levels (5 dB, 15 dB, 25 dB, and clean) of Signal-to-Noise Ratio (SNR) were introduced to corrupt the speech. Second, a new algorithm to estimate speech or no speech regions has been developed, implemented, and evaluated. Third, extensive simulations were carried out. It was found that the combination of the new algorithm, the proper selection of language model and a customized training of the speech recognizer based on clean speech yielded very high recognition rates, which are between 80% and 90% for the four different noisy conditions. Fourth, extensive comparative studies have also been carried out.
Keywords: Non-stationary, speech recognition, voice commands.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15231337 Automatic Lip Contour Tracking and Visual Character Recognition for Computerized Lip Reading
Authors: Harshit Mehrotra, Gaurav Agrawal, M.C. Srivastava
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Computerized lip reading has been one of the most actively researched areas of computer vision in recent past because of its crime fighting potential and invariance to acoustic environment. However, several factors like fast speech, bad pronunciation, poor illumination, movement of face, moustaches and beards make lip reading difficult. In present work, we propose a solution for automatic lip contour tracking and recognizing letters of English language spoken by speakers using the information available from lip movements. Level set method is used for tracking lip contour using a contour velocity model and a feature vector of lip movements is then obtained. Character recognition is performed using modified k nearest neighbor algorithm which assigns more weight to nearer neighbors. The proposed system has been found to have accuracy of 73.3% for character recognition with speaker lip movements as the only input and without using any speech recognition system in parallel. The approach used in this work is found to significantly solve the purpose of lip reading when size of database is small.Keywords: Contour Velocity Model, Lip Contour Tracking, LipReading, Visual Character Recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23921336 OCR for Script Identification of Hindi (Devnagari) Numerals using Feature Sub Selection by Means of End-Point with Neuro-Memetic Model
Authors: Banashree N. P., R. Vasanta
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Recognition of Indian languages scripts is challenging problems. In Optical Character Recognition [OCR], a character or symbol to be recognized can be machine printed or handwritten characters/numerals. There are several approaches that deal with problem of recognition of numerals/character depending on the type of feature extracted and different way of extracting them. This paper proposes a recognition scheme for handwritten Hindi (devnagiri) numerals; most admired one in Indian subcontinent. Our work focused on a technique in feature extraction i.e. global based approach using end-points information, which is extracted from images of isolated numerals. These feature vectors are fed to neuro-memetic model [18] that has been trained to recognize a Hindi numeral. The archetype of system has been tested on varieties of image of numerals. . In proposed scheme data sets are fed to neuro-memetic algorithm, which identifies the rule with highest fitness value of nearly 100 % & template associates with this rule is nothing but identified numerals. Experimentation result shows that recognition rate is 92-97 % compared to other models.Keywords: OCR, Global Feature, End-Points, Neuro-Memetic model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17531335 Face Localization and Recognition in Varied Expressions and Illumination
Authors: Hui-Yu Huang, Shih-Hang Hsu
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In this paper, we propose a robust scheme to work face alignment and recognition under various influences. For face representation, illumination influence and variable expressions are the important factors, especially the accuracy of facial localization and face recognition. In order to solve those of factors, we propose a robust approach to overcome these problems. This approach consists of two phases. One phase is preprocessed for face images by means of the proposed illumination normalization method. The location of facial features can fit more efficient and fast based on the proposed image blending. On the other hand, based on template matching, we further improve the active shape models (called as IASM) to locate the face shape more precise which can gain the recognized rate in the next phase. The other phase is to process feature extraction by using principal component analysis and face recognition by using support vector machine classifiers. The results show that this proposed method can obtain good facial localization and face recognition with varied illumination and local distortion.
Keywords: Gabor filter, improved active shape model (IASM), principal component analysis (PCA), face alignment, face recognition, support vector machine (SVM)
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14811334 Fitness Action Recognition Based on MediaPipe
Authors: Zixuan Xu, Yichun Lou, Yang Song, Zihuai Lin
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MediaPipe is an open-source machine learning computer vision framework that can be ported into a multi-platform environment, which makes it easier to use it to recognize human activity. Based on this framework, many human recognition systems have been created, but the fundamental issue is the recognition of human behavior and posture. In this paper, two methods are proposed to recognize human gestures based on MediaPipe, the first one uses the Adaptive Boosting algorithm to recognize a series of fitness gestures, and the second one uses the Fast Dynamic Time Warping algorithm to recognize 413 continuous fitness actions. These two methods are also applicable to any human posture movement recognition.
Keywords: Computer Vision, MediaPipe, Adaptive Boosting, Fast Dynamic Time Warping.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8011333 Smartphone-Based Human Activity Recognition by Machine Learning Methods
Authors: Yanting Cao, Kazumitsu Nawata
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As smartphones are continually upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described more refined, complex and detailed. In this context, we analyzed a set of experimental data, obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model become extremely challenging. After a series of feature selection and parameters adjustments, a well-performed SVM classifier has been trained.
Keywords: smart sensors, human activity recognition, artificial intelligence, SVM
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6181332 Speech Coding and Recognition
Authors: M. Satya Sai Ram, P. Siddaiah, M. Madhavi Latha
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This paper investigates the performance of a speech recognizer in an interactive voice response system for various coded speech signals, coded by using a vector quantization technique namely Multi Switched Split Vector Quantization Technique. The process of recognizing the coded output can be used in Voice banking application. The recognition technique used for the recognition of the coded speech signals is the Hidden Markov Model technique. The spectral distortion performance, computational complexity, and memory requirements of Multi Switched Split Vector Quantization Technique and the performance of the speech recognizer at various bit rates have been computed. From results it is found that the speech recognizer is showing better performance at 24 bits/frame and it is found that the percentage of recognition is being varied from 100% to 93.33% for various bit rates.Keywords: Linear predictive coding, Speech Recognition, Voice banking, Multi Switched Split Vector Quantization, Hidden Markov Model, Linear Predictive Coefficients.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18321331 Recognition Machine (RM) for On-line and Isolated Flight Deck Officer (FDO) Gestures
Authors: Deniz T. Sodiri, Venkat V S S Sastry
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The paper presents an on-line recognition machine (RM) for continuous/isolated, dynamic and static gestures that arise in Flight Deck Officer (FDO) training. RM is based on generic pattern recognition framework. Gestures are represented as templates using summary statistics. The proposed recognition algorithm exploits temporal and spatial characteristics of gestures via dynamic programming and Markovian process. The algorithm predicts corresponding index of incremental input data in the templates in an on-line mode. Accumulated consistency in the sequence of prediction provides a similarity measurement (Score) between input data and the templates. The algorithm provides an intuitive mechanism for automatic detection of start/end frames of continuous gestures. In the present paper, we consider isolated gestures. The performance of RM is evaluated using four datasets - artificial (W TTest), hand motion (Yang) and FDO (tracker, vision-based ). RM achieves comparable results which are in agreement with other on-line and off-line algorithms such as hidden Markov model (HMM) and dynamic time warping (DTW). The proposed algorithm has the additional advantage of providing timely feedback for training purposes.Keywords: On-line Recognition Algorithm, IsolatedDynamic/Static Gesture Recognition, On-line Markovian/DynamicProgramming, Training in Virtual Environments.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14521330 Transfer Knowledge from Multiple Source Problems to a Target Problem in Genetic Algorithm
Authors: Tami Alghamdi, Terence Soule
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To study how to transfer knowledge from multiple source problems to the target problem, we modeled the Transfer Learning (TL) process using Genetic Algorithms as the model solver. TL is the process that aims to transfer learned data from one problem to another problem. The TL process aims to help Machine Learning (ML) algorithms find a solution to the problems. The Genetic Algorithms (GA) give researchers access to information that we have about how the old problem is solved. In this paper, we have five different source problems, and we transfer the knowledge to the target problem. We studied different scenarios of the target problem. The results showed that combined knowledge from multiple source problems improves the GA performance. Also, the process of combining knowledge from several problems results in promoting diversity of the transferred population.
Keywords: Transfer Learning, Multiple Sources, Knowledge Transfer, Domain Adaptation, Source, Target.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3281329 Local Steerable Pyramid Binary Pattern Sequence LSPBPS for Face Recognition Method
Authors: Mohamed El Aroussi, Mohammed El Hassouni, Sanaa Ghouzali, Mohammed Rziza, Driss Aboutajdine
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In this paper the problem of face recognition under variable illumination conditions is considered. Most of the works in the literature exhibit good performance under strictly controlled acquisition conditions, but the performance drastically drop when changes in pose and illumination occur, so that recently number of approaches have been proposed to deal with such variability. The aim of this work is to introduce an efficient local appearance feature extraction method based steerable pyramid (SP) for face recognition. Local information is extracted from SP sub-bands using LBP(Local binary Pattern). The underlying statistics allow us to reduce the required amount of data to be stored. The experiments carried out on different face databases confirm the effectiveness of the proposed approach.
Keywords: Face recognition (FR), Steerable pyramid (SP), localBinary Pattern (LBP).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21701328 Effects of Recognition of Customer Feedback on Relationships between Emotional Labor and Job Satisfaction: Focusing on a Call Center that Offers Professional Services
Authors: Kiyoko Yoshimura, Yasunobu Kino
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Focusing on professional call centers where workers with expertise perform services, this study aims to clarify the relationships between emotional labor and job satisfaction and the effects of recognition of customer feedback. Since the professional call center operators consist of professional license holders (qualification holders) and those who do not (non-holders), the following three points are analyzed in the two groups by using covariance structure analysis and simultaneous multi-population analysis: 1) The relationship between emotional labor and job satisfaction, 2) customer feedback and job satisfaction, and 3) the intermediation effect between the emotional labor of customer feedback and job satisfaction. The following results are obtained: i) No direct effect is found between job satisfaction and emotional labor for qualification holders and non-holders, ii) for qualification holders and non-holders, recognition of positive feedback and recognition of negative feedback had positive and negative effects on job satisfaction, respectively, iii) for qualification and non-holders, “consideration for colleagues” influences job satisfaction by recognizing positive feedback, and iv) only for qualification holders, the factors “customer-oriented emotional expression” and “emotional disharmony” have a positive and negative effect on job satisfaction, respectively, through recognition of positive feedback and recognition of negative feedback.
Keywords: Call center, emotional labor, professional service, job satisfaction, customer feedback.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 371327 Liveness Detection for Embedded Face Recognition System
Authors: Hyung-Keun Jee, Sung-Uk Jung, Jang-Hee Yoo
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To increase reliability of face recognition system, the system must be able to distinguish real face from a copy of face such as a photograph. In this paper, we propose a fast and memory efficient method of live face detection for embedded face recognition system, based on the analysis of the movement of the eyes. We detect eyes in sequential input images and calculate variation of each eye region to determine whether the input face is a real face or not. Experimental results show that the proposed approach is competitive and promising for live face detection.Keywords: Liveness Detection, Eye detection, SQI.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 31651326 Reduced Dynamic Time Warping for Handwriting Recognition Based on Multidimensional Time Series of a Novel Pen Device
Authors: Muzaffar Bashir, Jürgen Kempf
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The purpose of this paper is to present a Dynamic Time Warping technique which reduces significantly the data processing time and memory size of multi-dimensional time series sampled by the biometric smart pen device BiSP. The acquisition device is a novel ballpoint pen equipped with a diversity of sensors for monitoring the kinematics and dynamics of handwriting movement. The DTW algorithm has been applied for time series analysis of five different sensor channels providing pressure, acceleration and tilt data of the pen generated during handwriting on a paper pad. But the standard DTW has processing time and memory space problems which limit its practical use for online handwriting recognition. To face with this problem the DTW has been applied to the sum of the five sensor signals after an adequate down-sampling of the data. Preliminary results have shown that processing time and memory size could significantly be reduced without deterioration of performance in single character and word recognition. Further excellent accuracy in recognition was achieved which is mainly due to the reduced dynamic time warping RDTW technique and a novel pen device BiSP.Keywords: Biometric character recognition, biometric person authentication, biometric smart pen BiSP, dynamic time warping DTW, online-handwriting recognition, multidimensional time series.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23951325 Voice Command Recognition System Based on MFCC and VQ Algorithms
Authors: Mahdi Shaneh, Azizollah Taheri
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The goal of this project is to design a system to recognition voice commands. Most of voice recognition systems contain two main modules as follow “feature extraction" and “feature matching". In this project, MFCC algorithm is used to simulate feature extraction module. Using this algorithm, the cepstral coefficients are calculated on mel frequency scale. VQ (vector quantization) method will be used for reduction of amount of data to decrease computation time. In the feature matching stage Euclidean distance is applied as similarity criterion. Because of high accuracy of used algorithms, the accuracy of this voice command system is high. Using these algorithms, by at least 5 times repetition for each command, in a single training session, and then twice in each testing session zero error rate in recognition of commands is achieved.Keywords: MFCC, Vector quantization, Vocal tract, Voicecommand.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 31421324 Particle Filter Supported with the Neural Network for Aircraft Tracking Based on Kernel and Active Contour
Authors: Mohammad Izadkhah, Mojtaba Hoseini, Alireza Khalili Tehrani
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In this paper we presented a new method for tracking flying targets in color video sequences based on contour and kernel. The aim of this work is to overcome the problem of losing target in changing light, large displacement, changing speed, and occlusion. The proposed method is made in three steps, estimate the target location by particle filter, segmentation target region using neural network and find the exact contours by greedy snake algorithm. In the proposed method we have used both region and contour information to create target candidate model and this model is dynamically updated during tracking. To avoid the accumulation of errors when updating, target region given to a perceptron neural network to separate the target from background. Then its output used for exact calculation of size and center of the target. Also it is used as the initial contour for the greedy snake algorithm to find the exact target's edge. The proposed algorithm has been tested on a database which contains a lot of challenges such as high speed and agility of aircrafts, background clutter, occlusions, camera movement, and so on. The experimental results show that the use of neural network increases the accuracy of tracking and segmentation.Keywords: Video tracking, particle filter, greedy snake, neural network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11851323 Optimal Feature Extraction Dimension in Finger Vein Recognition Using Kernel Principal Component Analysis
Authors: Amir Hajian, Sepehr Damavandinejadmonfared
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In this paper the issue of dimensionality reduction is investigated in finger vein recognition systems using kernel Principal Component Analysis (KPCA). One aspect of KPCA is to find the most appropriate kernel function on finger vein recognition as there are several kernel functions which can be used within PCA-based algorithms. In this paper, however, another side of PCA-based algorithms -particularly KPCA- is investigated. The aspect of dimension of feature vector in PCA-based algorithms is of importance especially when it comes to the real-world applications and usage of such algorithms. It means that a fixed dimension of feature vector has to be set to reduce the dimension of the input and output data and extract the features from them. Then a classifier is performed to classify the data and make the final decision. We analyze KPCA (Polynomial, Gaussian, and Laplacian) in details in this paper and investigate the optimal feature extraction dimension in finger vein recognition using KPCA.
Keywords: Biometrics, finger vein recognition, Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19541322 Fast Algorithm of Infrared Point Target Detection in Fluctuant Background
Authors: Yang Weiping, Zhang Zhilong, Li Jicheng, Chen Zengping, He Jun
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The background estimation approach using a small window median filter is presented on the bases of analyzing IR point target, noise and clutter model. After simplifying the two-dimensional filter, a simple method of adopting one-dimensional median filter is illustrated to make estimations of background according to the characteristics of IR scanning system. The adaptive threshold is used to segment canceled image in the background. Experimental results show that the algorithm achieved good performance and satisfy the requirement of big size image-s real-time processing.Keywords: Point target, background estimation, median filter, adaptive threshold, target detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18321321 A Supervised Text-Independent Speaker Recognition Approach
Authors: Tudor Barbu
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We provide a supervised speech-independent voice recognition technique in this paper. In the feature extraction stage we propose a mel-cepstral based approach. Our feature vector classification method uses a special nonlinear metric, derived from the Hausdorff distance for sets, and a minimum mean distance classifier.
Keywords: Text-independent speaker recognition, mel cepstral analysis, speech feature vector, Hausdorff-based metric, supervised classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18221320 Motion Recognition Based On Fuzzy WP Feature Extraction Approach
Authors: Keun-Chang Kwak
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This paper is concerned with motion recognition based fuzzy WP(Wavelet Packet) feature extraction approach from Vicon physical data sets. For this purpose, we use an efficient fuzzy mutual-information-based WP transform for feature extraction. This method estimates the required mutual information using a novel approach based on fuzzy membership function. The physical action data set includes 10 normal and 10 aggressive physical actions that measure the human activity. The data have been collected from 10 subjects using the Vicon 3D tracker. The experiments consist of running, seating, and walking as physical activity motion among various activities. The experimental results revealed that the presented feature extraction approach showed good recognition performance.
Keywords: Motion recognition, fuzzy wavelet packet, Vicon physical data.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16341319 Rotation Invariant Face Recognition Based on Hybrid LPT/DCT Features
Authors: Rehab F. Abdel-Kader, Rabab M. Ramadan, Rawya Y. Rizk
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The recognition of human faces, especially those with different orientations is a challenging and important problem in image analysis and classification. This paper proposes an effective scheme for rotation invariant face recognition using Log-Polar Transform and Discrete Cosine Transform combined features. The rotation invariant feature extraction for a given face image involves applying the logpolar transform to eliminate the rotation effect and to produce a row shifted log-polar image. The discrete cosine transform is then applied to eliminate the row shift effect and to generate the low-dimensional feature vector. A PSO-based feature selection algorithm is utilized to search the feature vector space for the optimal feature subset. Evolution is driven by a fitness function defined in terms of maximizing the between-class separation (scatter index). Experimental results, based on the ORL face database using testing data sets for images with different orientations; show that the proposed system outperforms other face recognition methods. The overall recognition rate for the rotated test images being 97%, demonstrating that the extracted feature vector is an effective rotation invariant feature set with minimal set of selected features.Keywords: Discrete Cosine Transform, Face Recognition, Feature Extraction, Log Polar Transform, Particle SwarmOptimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18631318 Face Recognition Using Morphological Shared-weight Neural Networks
Authors: Hossein Sahoolizadeh, Mahdi Rahimi, Hamid Dehghani
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We introduce an algorithm based on the morphological shared-weight neural network. Being nonlinear and translation-invariant, the MSNN can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The output is then learned by interacting with the classification process. The feature extraction and classification networks are trained together, allowing the MSNN to simultaneously learn feature extraction and classification for a face. For evaluation, we test for robustness under variations in gray levels and noise while varying the network-s configuration to optimize recognition efficiency and processing time. Results show that the MSNN performs better for grayscale image pattern classification than ordinary neural networks.Keywords: Face recognition, Neural Networks, Multi-layer Perceptron, masking.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1507