This document summarizes and compares different classification techniques for face recognition using Support Vector Machines (SVM) and Incremental Support Vector Machines (ISVM). It first provides background on SVM and ISVM, describing them as prominent machine learning techniques for classification tasks. It then reviews related work applying SVM and ISVM to face recognition, including using 2D and 3D facial features, Gabor features to classify expressions, and incremental learning algorithms. The document analyzes the pros and cons of each technique.
Template matching is a technique used in computer vision to find sub-images in a target image that match a template image. It involves moving the template over the target image and calculating a measure of similarity at each position. This is computationally expensive. Template matching can be done at the pixel level or on higher-level features and regions. Various measures are used to quantify the similarity or dissimilarity between images during the matching process. Template matching has applications in areas like object detection but faces challenges with noise, occlusions, and variations in scale and rotation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET- Facial Gesture Recognition using Surface EMG and Multiclass Support Ve...IRJET Journal
This document presents a study on facial gesture recognition using surface electromyography (EMG) signals and a multiclass support vector machine (SVM) classifier. EMG signals were collected from facial muscles for four expressions: frowning, puckering, smiling, and chewing. Time-domain features like root mean square, variance, and standard deviation were extracted from the EMG signals. These features were classified using SVM and k-nearest neighbors algorithms. SVM achieved the highest accuracy of 92.76% for recognizing the four facial expressions based on the EMG signals. The results indicate this EMG-based approach can efficiently predict different facial expressions.
Emotion Detection using Artificial Intelligence presentation by Aryan Trisal.
In this ppt you will learn about emotion detection using AI and how will it change the world.
IF U WANT A PPT MADE AT VERY LOW PRICES CONTACT ME ON LINKEDIN -www.linkedin.com/in/aryan-trisal-420253190
Exploring the Impact of Magnitude- and Direction-based Loss Function on the P...Dr. Amarjeet Singh
Researches on predicting prices (as time series) from deep learning models usually use a magnitude-based error measurement (such as ). However, in trading, the error in the predicted direction could affect trading results much more than the magnitude error. Few works consider the impact of ill-predicted trading direction as part of the error measurement.
In this work, we first find parameter sets of LSTM and TCN models with low magnitude-based error measurement, and then calculate the profitability using program trading. Relationships between profitability and error measurements are analyzed.
We also propose a new loss function considering both directional and magnitude error for previous models for re-evaluation. Three commodities are tested: gold, soybean, and crude oil (from GLOBEX). Our findings are: with given parameter sets, if merchandise (gold and soybean) is of low averaged magnitude error, then its profitability is more stable. The proposed loss function can further improve profitability. If it is of larger magnitude error (crude oil), then its profitability is unstable, and the proposed loss function cannot improve nor stabilize the profitability.
Furthermore, the relationship between profitability and error measurement for models of LSTM and TCN with or without customized loss function is not, as commonly believed, highly positively correlated (i.e., the more precise the predicted value, the more trading profit) since the correlation coefficients are rarely higher than 0.5 in all our experiments. However, the customized loss functions perform better in TCN than in LSTM.
Facial emoji recognition is a human computer interaction system. In recent times, automatic face recognition or facial expression recognition has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and similar fields. Facial emoji recognizer is an end user application which detects the expression of the person in the video being captured by the camera. The smiley relevant to the expression of the person in the video is shown on the screen which changes with the change in the expressions. Facial expressions are important in human communication and interactions. Also, they are used as an important tool in studies about behavior and in medical fields. Facial emoji recognizer provides a fast and practical approach for non meddlesome emotion detection. The purpose was to develop an intelligent system for facial based expression classification using CNN algorithm. Haar classifier is used for face detection and CNN algorithm is utilized for the expression detection and giving the emoticon relevant to the expression as the output. N. Swapna Goud | K. Revanth Reddy | G. Alekhya | G. S. Sucheta ""Facial Emoji Recognition"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23166.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23166/facial-emoji-recognition/n-swapna-goud
International journal of signal and image processing issues vol 2015 - no 1...sophiabelthome
This document reviews commonly used calibration patterns for camera calibration and image rectification. It discusses traditional 2D and 3D patterns using points, lines or geometric shapes. Structured light patterns using diffractive optical elements are also presented. Extraction of pattern data is important and can be done through intensity-based subpixel detection or edge detection techniques. Accuracy is evaluated using metrics like root mean square error. Image rectification transforms distorted images into rectilinear images by modeling and removing lens distortion.
IRJET - Skin Disease Predictor using Deep LearningIRJET Journal
This document presents a skin disease prediction system built using a deep learning model. The system was trained on the Harvard HAM dataset containing images of 7 common skin diseases. Data augmentation techniques like rotation, shearing, zooming were used to improve the quality and size of the dataset. A convolutional neural network model with convolution, pooling, ReLU and fully connected layers was developed using Keras. The model achieved an accuracy of 82% and was integrated into a web-based user interface to allow users to upload images for disease prediction. Further improvements to increase accuracy require enhancing the model with more data and computational resources.
Template matching is a technique used in computer vision to find sub-images in a target image that match a template image. It involves moving the template over the target image and calculating a measure of similarity at each position. This is computationally expensive. Template matching can be done at the pixel level or on higher-level features and regions. Various measures are used to quantify the similarity or dissimilarity between images during the matching process. Template matching has applications in areas like object detection but faces challenges with noise, occlusions, and variations in scale and rotation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET- Facial Gesture Recognition using Surface EMG and Multiclass Support Ve...IRJET Journal
This document presents a study on facial gesture recognition using surface electromyography (EMG) signals and a multiclass support vector machine (SVM) classifier. EMG signals were collected from facial muscles for four expressions: frowning, puckering, smiling, and chewing. Time-domain features like root mean square, variance, and standard deviation were extracted from the EMG signals. These features were classified using SVM and k-nearest neighbors algorithms. SVM achieved the highest accuracy of 92.76% for recognizing the four facial expressions based on the EMG signals. The results indicate this EMG-based approach can efficiently predict different facial expressions.
Emotion Detection using Artificial Intelligence presentation by Aryan Trisal.
In this ppt you will learn about emotion detection using AI and how will it change the world.
IF U WANT A PPT MADE AT VERY LOW PRICES CONTACT ME ON LINKEDIN -www.linkedin.com/in/aryan-trisal-420253190
Exploring the Impact of Magnitude- and Direction-based Loss Function on the P...Dr. Amarjeet Singh
Researches on predicting prices (as time series) from deep learning models usually use a magnitude-based error measurement (such as ). However, in trading, the error in the predicted direction could affect trading results much more than the magnitude error. Few works consider the impact of ill-predicted trading direction as part of the error measurement.
In this work, we first find parameter sets of LSTM and TCN models with low magnitude-based error measurement, and then calculate the profitability using program trading. Relationships between profitability and error measurements are analyzed.
We also propose a new loss function considering both directional and magnitude error for previous models for re-evaluation. Three commodities are tested: gold, soybean, and crude oil (from GLOBEX). Our findings are: with given parameter sets, if merchandise (gold and soybean) is of low averaged magnitude error, then its profitability is more stable. The proposed loss function can further improve profitability. If it is of larger magnitude error (crude oil), then its profitability is unstable, and the proposed loss function cannot improve nor stabilize the profitability.
Furthermore, the relationship between profitability and error measurement for models of LSTM and TCN with or without customized loss function is not, as commonly believed, highly positively correlated (i.e., the more precise the predicted value, the more trading profit) since the correlation coefficients are rarely higher than 0.5 in all our experiments. However, the customized loss functions perform better in TCN than in LSTM.
Facial emoji recognition is a human computer interaction system. In recent times, automatic face recognition or facial expression recognition has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and similar fields. Facial emoji recognizer is an end user application which detects the expression of the person in the video being captured by the camera. The smiley relevant to the expression of the person in the video is shown on the screen which changes with the change in the expressions. Facial expressions are important in human communication and interactions. Also, they are used as an important tool in studies about behavior and in medical fields. Facial emoji recognizer provides a fast and practical approach for non meddlesome emotion detection. The purpose was to develop an intelligent system for facial based expression classification using CNN algorithm. Haar classifier is used for face detection and CNN algorithm is utilized for the expression detection and giving the emoticon relevant to the expression as the output. N. Swapna Goud | K. Revanth Reddy | G. Alekhya | G. S. Sucheta ""Facial Emoji Recognition"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23166.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23166/facial-emoji-recognition/n-swapna-goud
International journal of signal and image processing issues vol 2015 - no 1...sophiabelthome
This document reviews commonly used calibration patterns for camera calibration and image rectification. It discusses traditional 2D and 3D patterns using points, lines or geometric shapes. Structured light patterns using diffractive optical elements are also presented. Extraction of pattern data is important and can be done through intensity-based subpixel detection or edge detection techniques. Accuracy is evaluated using metrics like root mean square error. Image rectification transforms distorted images into rectilinear images by modeling and removing lens distortion.
IRJET - Skin Disease Predictor using Deep LearningIRJET Journal
This document presents a skin disease prediction system built using a deep learning model. The system was trained on the Harvard HAM dataset containing images of 7 common skin diseases. Data augmentation techniques like rotation, shearing, zooming were used to improve the quality and size of the dataset. A convolutional neural network model with convolution, pooling, ReLU and fully connected layers was developed using Keras. The model achieved an accuracy of 82% and was integrated into a web-based user interface to allow users to upload images for disease prediction. Further improvements to increase accuracy require enhancing the model with more data and computational resources.
FIDUCIAL POINTS DETECTION USING SVM LINEAR CLASSIFIERScsandit
Currently, there is a growing interest from the scientific and/or industrial community in respect
to methods that offer solutions to the problem of fiducial points detection in human faces. Some
methods use the SVM for classification, but we observed that some formulations of optimization
problems were not discussed. In this article, we propose to investigate the performance of
mathematical formulation C-SVC when applied in fiducial point detection system. Futhermore,
we explore new parameters for training the proposed system. The performance of the proposed
system is evaluated in a fiducial points detection problem. The results demonstrate that the
method is competitive.
Automatic analysis of smoothing techniques by simulation model based real tim...ijesajournal
The pivotal research work that has been carried out and described in this literature acknowledges the
importance of various smoothing techniques for processing 3D human faces from 2.5D range face images.
The smoothing techniques have been developed and implemented using MATLAB-Simulink for real time
processing in embedded system. In addition, the significance of smoothed 2.5D range image over original
face range image has been discovered as well as its time complexity has also been reported with array of
experiments. The variations in time complexities are also accomplished using different optimization levels
and execution modes. A set of filtering techniques such as, Max filter, Min filter, Median filter, Mean filter,
Mid-point filter and Gaussian filter, have been designed and illustrated using Simulink model. The model
takes depth face image (i.e. the range face image) as input in real time and presents the improvement over
original face images. In the design flow, the performance of every block has also been characterized by
range face images from Frav3D, GavabDB, and Bosphorus databases. In the experimental section of this
research article, an array of performance analysis for these smoothing techniques with variation of
frameworks is explained.
A FUZZY INTERACTIVE BI-OBJECTIVE MODEL FOR SVM TO IDENTIFY THE BEST COMPROMIS...ijfls
A support vector machine (SVM) learns the decision surface from two different classes of the input points. In several applications, some of the input points are misclassified and each is not fully allocated to either of these two groups. In this paper a bi-objective quadratic programming model with fuzzy parameters is utilized and different feature quality measures are optimized simultaneously. An α-cut is defined to transform the fuzzy model to a family of classical bi-objective quadratic programming problems. The weighting method is used to optimize each of these problems. For the proposed fuzzy bi-objective quadratic programming model, a major contribution will be added by obtaining different effective support vectors due to changes in weighting values. The experimental results, show the effectiveness of the α-cut with the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions. The main contribution of this paper includes constructing a utility function for measuring the degree of infection with coronavirus disease (COVID-19).
The purpose research is to develop the decision model of Multi-Criteria Group Decision Making (MCGDM) into Interval Value Fuzzy Multi-Criteria Group Decision Making (IV-FMCGDM), while the specific purpose is to construct decision-making model of Adaptive Interval Value Fuzzy Analytic Hierarchy Process (AIV- FAHP) uses Triangular Fuzzy Number (TFN) and group decision aggregation functions using Interval Value Geometric Means Aggregation (IV-GMA). The novelty research is to study the concept of group decision making by improving the middle point on the Interval Value Triangular Fuzzy Number (IV TFN). It provides more accurate modeling, and better rating performance, and more effective linguistic representation. This research produced a new decision-making model and algorithm based on AIV-FAHP used to measure the quality of e-learning.
This document discusses using an adaptive boosted support vector machine to classify potential direct marketing consumers using bank customer data. It compares the performance of an ordinary SVM classifier to an SVM classifier combined with an Adaboost algorithm. The Adaboost-SVM approach achieved higher accuracy (95.07%) and sensitivity (91.65%) compared to the ordinary SVM (91.67% accuracy and 83.80% sensitivity) in predicting customer subscription prospects from a dataset of over 9,000 records with 20 attributes. The results showed that ensemble methods like Adaboost can improve the performance of a single SVM classifier.
Image Inpainting System Model Based on Evaluationijcsa
Image segmentation algorithm and inpainting algorithm are the key ingredients in the process of inpainting
after studying many image-inpainting algorithms. Therefore, analyzing, comparing and verifying the
segment algorithm and inpainting algorithms, the system model which owns segment and repair evaluation
function is constructed, so it can optimize the segmentation algorithms and the inpainting algorithms;
finally make the inpainting result better. There is segmentation module and inpainting module in the system
model, the former is to segment damaged area, and the latter is to repair image. They adopt expert system,
which extract image characteristics and optimize segmentation algorithms and inpainting algorithms by
heuristic rules in the knowledge database, evaluate the result of the inpainting which can feedback the
heuristic rules for selecting better algorithms, finally adopt the best segmentation and inpainting algorithm.
System model synthesizes two key ingredients of segmentation and inpainting, so that it can enhance the
inpainting effect, and that the system will be constructed actually need to further study and to carry out.
1) The document presents a novel approach for pose estimation, normalization, and face recognition using multi-class SVM, affine transformation, and DCT.
2) The training section uses affine transformation for pose normalization and DCT for illumination removal. During testing, multi-class SVM estimates pose and affine transformation and DCT are applied to normalize pose and remove illumination before recognition.
3) PCA is used to extract features for matching during recognition. Experimental results on FERET datasets show the approach achieves 100% recognition rate after pose normalization and illumination removal.
IRJET - An Enhanced Approach for Extraction of Text from an Image using Fuzzy...IRJET Journal
This document presents an approach for extracting text from images using fuzzy logic. It involves preprocessing the image to remove noise, segmenting the image to extract individual characters, and then using fuzzy logic to identify the characters by comparing segmented characters to trained data and determining the degree of matching. The key steps are pre-processing, segmentation, feature extraction using techniques like statistical and geometrical features, classification using a convolutional neural network, and then using fuzzy logic to accurately identify characters by finding the highest matching value between segmented and trained characters. The goal is to recognize and extract text from the image in an editable format.
IRJET - Computer-Assisted ALL, AML, CLL, CML Detection and Counting for D...IRJET Journal
This document describes a computer-assisted method for detecting and counting four types of blood cancer (ALL, AML, CLL, CML) from microscopic blood images. The method first segments the image to identify white blood cells, then extracts lymphocytes. Shape and color features of the lymphocytes are used to classify them as normal or blast cells using SVM. The system was found to be more accurate and fast compared to manual identification methods. It aims to automatically diagnose blood cancers from images in a time-efficient and accurate manner.
FINGERPRINT MATCHING USING HYBRID SHAPE AND ORIENTATION DESCRIPTOR -AN IMPROV...IJCI JOURNAL
Fingerprint recognition is a promising factor for the Biometric Identification and authentication process.
Fingerprints are broadly used for personal identification due to its feasibility, distinctiveness, permanence,
accuracy and acceptability. This paper proposes a way to improve the Equal Error Rate (EER) in
fingerprint matching techniques in the domain of hybrid shape and orientation descriptor. This type of
fingerprint matching domain is popular due to capability of filtering false and strange minutiae pairings.
EER is calculated by using FMR and FNMR to check the performance of proposed technique.
A novel tool for stereo matching of imageseSAT Journals
Abstract Stereo matching techniques play an important role in many real world applications like robot stereo vision and image sequence analysis. From given pair of stereo pairs of images, it is possible to have matching techniques to obtain image descriptors or phenomena to compare the images. The goal of stereo matching can be achieved using either relational matching or feature or signal. However, the signal approach is most widely used. Recently Lemmens [10] provided a comprehensive review of many stereo matching techniques. In this paper we implement the techniques that can help in the real world. We build a prototype application that demonstrates the proof of concept. The empirical results revealed that the proposed application has good utility. Keywords – Stereo images, stereo matching,
This document summarizes a research paper on tracking multiple targets using the mean shift algorithm. It begins by stating that multi-target tracking is challenging due to factors like noise, clutter, occlusions, and sudden changes in velocity. The mean shift algorithm is then introduced as a kernel-based tracking method that works by iteratively shifting target locations to their mean shifts. Targets are represented using histograms within elliptical regions. The Bhattacharyya coefficient is used to measure similarity between target models and candidates. Experimental results on a video sequence show the algorithm can accurately track targets under small displacements but performance degrades for large displacements, fast motion, or occlusions. In conclusion, the mean shift algorithm provides a simple method for multi
1) The document discusses stereo matching techniques for images, which can be used in applications like robot vision. It reviews different stereo matching approaches like feature matching, relational matching, and signal matching.
2) It describes implementing a prototype application to demonstrate stereo matching between two images. The application allows navigating images, identifies points for matching, and shows statistics on the images and matched points.
3) Test results on sample images show the interface of the application with image previews, point matching details in tables, and overlapped images after swiping between images. The empirical results found the proposed application has good utility for stereo matching.
Fuzzy Region Merging Using Fuzzy Similarity Measurement on Image Segmentation IJECEIAES
Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively.
One-Sample Face Recognition Using HMM Model of Fiducial AreasCSCJournals
In most real world applications, multiple image samples of individuals are not easy to collate for direct implementation of recognition or verification systems. Therefore there is a need to perform these tasks even if only one training sample per person is available. This paper describes an effective algorithm for recognition and verification with one sample image per class. It uses two dimensional discrete wavelet transform (2D DWT) to extract features from images and hidden Markov model (HMM) was used for training, recognition and classification. It was tested with a subset of the AT&T database and up to 90% correct classification (Hit) and false acceptance rate (FAR) of 0.02% was achieved.
Multibiometric systems are expected to be more reliable than unimodal biometric systems for personal identification due to the presence of multiple, fairly independent pieces of evidence e.g. Unique Identification Project "Aadhaar" of Government of India. In this paper, we present a novel wavelet based technique to perform fusion at the feature level and score level by considering two biometric modalities, face and fingerprint. The results indicate that the proposed technique can lead to substantial improvement in multimodal matching performance. The proposed technique is simple because of no preprocessing of raw biometric traits as well as no feature and score normalization.
IRJET- A Plant Identification and Recommendation SystemIRJET Journal
This document describes a plant identification and recommendation system that uses image recognition techniques. The system takes an image of a leaf as input, preprocesses it by resizing, converting to grayscale, and extracting features. It then uses a convolutional neural network with the Inception-v3 model to identify the plant by comparing features to those in its database. Based on the identified plant, it recommends other plants that could grow in that location. The system is implemented as both a mobile app and web application to be accessible anywhere.
License plate recognition an insight to the proposed approach for plate local...Editor Jacotech
This document summarizes a journal article that proposes an approach for license plate localization and binarization in license plate recognition systems. The article describes the typical three-stage process of license plate recognition including localization, character segmentation, and character recognition. It then discusses challenges with existing localization approaches for Indian license plates due to variations in formats. The proposed approach exploits features like aspect ratio, texture, and color similarity to localize and extract license plates from images as a preprocessing step before character segmentation and recognition.
Cloud computing a services business application challengesEditor Jacotech
This document discusses challenges related to adopting Software as a Service (SaaS) business applications. It reviews literature that has identified key challenges such as data security, customization, and scalability. The document provides background on cloud computing models including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). It also discusses characteristics of the SaaS cloud computing model and the business value it provides.
A mobile monitoring and alert sms system with remote configuration – a case s...Editor Jacotech
This document describes a mobile monitoring and alert SMS system for tracking children's locations using Android devices. It has the following key points:
1. The system allows parents to monitor children's locations through SMS messages without requiring an internet connection, keeping costs low. It supports single position requests, scheduled location updates, and geofenced alerts when a child leaves a specified range.
2. The system architecture includes an Android app on the child's device to track location via GPS and respond to SMS requests, and a separate parent app to send requests and view locations. The child app can be configured to only accept requests from authorized phone numbers for security.
3. Remote monitoring is performed by the parent app sending SMS commands
This document summarizes and compares various encryption algorithms for providing security in cloud computing environments. It first discusses key-policy attribute-based encryption (KP-ABE) which associates attributes with keys and policies with data. It also covers ciphertext-policy attribute-based encryption (CP-ABE) which associates policies with ciphertext and attributes with keys. The document then analyzes expressive KP-ABE and ciphertext-policy attribute set-based encryption (CP-ASBE) which uses hierarchical attribute sets. It concludes that improving previous work to leverage hierarchical attribute sets of users may enhance security and access control when utilizing cloud computing.
FIDUCIAL POINTS DETECTION USING SVM LINEAR CLASSIFIERScsandit
Currently, there is a growing interest from the scientific and/or industrial community in respect
to methods that offer solutions to the problem of fiducial points detection in human faces. Some
methods use the SVM for classification, but we observed that some formulations of optimization
problems were not discussed. In this article, we propose to investigate the performance of
mathematical formulation C-SVC when applied in fiducial point detection system. Futhermore,
we explore new parameters for training the proposed system. The performance of the proposed
system is evaluated in a fiducial points detection problem. The results demonstrate that the
method is competitive.
Automatic analysis of smoothing techniques by simulation model based real tim...ijesajournal
The pivotal research work that has been carried out and described in this literature acknowledges the
importance of various smoothing techniques for processing 3D human faces from 2.5D range face images.
The smoothing techniques have been developed and implemented using MATLAB-Simulink for real time
processing in embedded system. In addition, the significance of smoothed 2.5D range image over original
face range image has been discovered as well as its time complexity has also been reported with array of
experiments. The variations in time complexities are also accomplished using different optimization levels
and execution modes. A set of filtering techniques such as, Max filter, Min filter, Median filter, Mean filter,
Mid-point filter and Gaussian filter, have been designed and illustrated using Simulink model. The model
takes depth face image (i.e. the range face image) as input in real time and presents the improvement over
original face images. In the design flow, the performance of every block has also been characterized by
range face images from Frav3D, GavabDB, and Bosphorus databases. In the experimental section of this
research article, an array of performance analysis for these smoothing techniques with variation of
frameworks is explained.
A FUZZY INTERACTIVE BI-OBJECTIVE MODEL FOR SVM TO IDENTIFY THE BEST COMPROMIS...ijfls
A support vector machine (SVM) learns the decision surface from two different classes of the input points. In several applications, some of the input points are misclassified and each is not fully allocated to either of these two groups. In this paper a bi-objective quadratic programming model with fuzzy parameters is utilized and different feature quality measures are optimized simultaneously. An α-cut is defined to transform the fuzzy model to a family of classical bi-objective quadratic programming problems. The weighting method is used to optimize each of these problems. For the proposed fuzzy bi-objective quadratic programming model, a major contribution will be added by obtaining different effective support vectors due to changes in weighting values. The experimental results, show the effectiveness of the α-cut with the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions. The main contribution of this paper includes constructing a utility function for measuring the degree of infection with coronavirus disease (COVID-19).
The purpose research is to develop the decision model of Multi-Criteria Group Decision Making (MCGDM) into Interval Value Fuzzy Multi-Criteria Group Decision Making (IV-FMCGDM), while the specific purpose is to construct decision-making model of Adaptive Interval Value Fuzzy Analytic Hierarchy Process (AIV- FAHP) uses Triangular Fuzzy Number (TFN) and group decision aggregation functions using Interval Value Geometric Means Aggregation (IV-GMA). The novelty research is to study the concept of group decision making by improving the middle point on the Interval Value Triangular Fuzzy Number (IV TFN). It provides more accurate modeling, and better rating performance, and more effective linguistic representation. This research produced a new decision-making model and algorithm based on AIV-FAHP used to measure the quality of e-learning.
This document discusses using an adaptive boosted support vector machine to classify potential direct marketing consumers using bank customer data. It compares the performance of an ordinary SVM classifier to an SVM classifier combined with an Adaboost algorithm. The Adaboost-SVM approach achieved higher accuracy (95.07%) and sensitivity (91.65%) compared to the ordinary SVM (91.67% accuracy and 83.80% sensitivity) in predicting customer subscription prospects from a dataset of over 9,000 records with 20 attributes. The results showed that ensemble methods like Adaboost can improve the performance of a single SVM classifier.
Image Inpainting System Model Based on Evaluationijcsa
Image segmentation algorithm and inpainting algorithm are the key ingredients in the process of inpainting
after studying many image-inpainting algorithms. Therefore, analyzing, comparing and verifying the
segment algorithm and inpainting algorithms, the system model which owns segment and repair evaluation
function is constructed, so it can optimize the segmentation algorithms and the inpainting algorithms;
finally make the inpainting result better. There is segmentation module and inpainting module in the system
model, the former is to segment damaged area, and the latter is to repair image. They adopt expert system,
which extract image characteristics and optimize segmentation algorithms and inpainting algorithms by
heuristic rules in the knowledge database, evaluate the result of the inpainting which can feedback the
heuristic rules for selecting better algorithms, finally adopt the best segmentation and inpainting algorithm.
System model synthesizes two key ingredients of segmentation and inpainting, so that it can enhance the
inpainting effect, and that the system will be constructed actually need to further study and to carry out.
1) The document presents a novel approach for pose estimation, normalization, and face recognition using multi-class SVM, affine transformation, and DCT.
2) The training section uses affine transformation for pose normalization and DCT for illumination removal. During testing, multi-class SVM estimates pose and affine transformation and DCT are applied to normalize pose and remove illumination before recognition.
3) PCA is used to extract features for matching during recognition. Experimental results on FERET datasets show the approach achieves 100% recognition rate after pose normalization and illumination removal.
IRJET - An Enhanced Approach for Extraction of Text from an Image using Fuzzy...IRJET Journal
This document presents an approach for extracting text from images using fuzzy logic. It involves preprocessing the image to remove noise, segmenting the image to extract individual characters, and then using fuzzy logic to identify the characters by comparing segmented characters to trained data and determining the degree of matching. The key steps are pre-processing, segmentation, feature extraction using techniques like statistical and geometrical features, classification using a convolutional neural network, and then using fuzzy logic to accurately identify characters by finding the highest matching value between segmented and trained characters. The goal is to recognize and extract text from the image in an editable format.
IRJET - Computer-Assisted ALL, AML, CLL, CML Detection and Counting for D...IRJET Journal
This document describes a computer-assisted method for detecting and counting four types of blood cancer (ALL, AML, CLL, CML) from microscopic blood images. The method first segments the image to identify white blood cells, then extracts lymphocytes. Shape and color features of the lymphocytes are used to classify them as normal or blast cells using SVM. The system was found to be more accurate and fast compared to manual identification methods. It aims to automatically diagnose blood cancers from images in a time-efficient and accurate manner.
FINGERPRINT MATCHING USING HYBRID SHAPE AND ORIENTATION DESCRIPTOR -AN IMPROV...IJCI JOURNAL
Fingerprint recognition is a promising factor for the Biometric Identification and authentication process.
Fingerprints are broadly used for personal identification due to its feasibility, distinctiveness, permanence,
accuracy and acceptability. This paper proposes a way to improve the Equal Error Rate (EER) in
fingerprint matching techniques in the domain of hybrid shape and orientation descriptor. This type of
fingerprint matching domain is popular due to capability of filtering false and strange minutiae pairings.
EER is calculated by using FMR and FNMR to check the performance of proposed technique.
A novel tool for stereo matching of imageseSAT Journals
Abstract Stereo matching techniques play an important role in many real world applications like robot stereo vision and image sequence analysis. From given pair of stereo pairs of images, it is possible to have matching techniques to obtain image descriptors or phenomena to compare the images. The goal of stereo matching can be achieved using either relational matching or feature or signal. However, the signal approach is most widely used. Recently Lemmens [10] provided a comprehensive review of many stereo matching techniques. In this paper we implement the techniques that can help in the real world. We build a prototype application that demonstrates the proof of concept. The empirical results revealed that the proposed application has good utility. Keywords – Stereo images, stereo matching,
This document summarizes a research paper on tracking multiple targets using the mean shift algorithm. It begins by stating that multi-target tracking is challenging due to factors like noise, clutter, occlusions, and sudden changes in velocity. The mean shift algorithm is then introduced as a kernel-based tracking method that works by iteratively shifting target locations to their mean shifts. Targets are represented using histograms within elliptical regions. The Bhattacharyya coefficient is used to measure similarity between target models and candidates. Experimental results on a video sequence show the algorithm can accurately track targets under small displacements but performance degrades for large displacements, fast motion, or occlusions. In conclusion, the mean shift algorithm provides a simple method for multi
1) The document discusses stereo matching techniques for images, which can be used in applications like robot vision. It reviews different stereo matching approaches like feature matching, relational matching, and signal matching.
2) It describes implementing a prototype application to demonstrate stereo matching between two images. The application allows navigating images, identifies points for matching, and shows statistics on the images and matched points.
3) Test results on sample images show the interface of the application with image previews, point matching details in tables, and overlapped images after swiping between images. The empirical results found the proposed application has good utility for stereo matching.
Fuzzy Region Merging Using Fuzzy Similarity Measurement on Image Segmentation IJECEIAES
Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively.
One-Sample Face Recognition Using HMM Model of Fiducial AreasCSCJournals
In most real world applications, multiple image samples of individuals are not easy to collate for direct implementation of recognition or verification systems. Therefore there is a need to perform these tasks even if only one training sample per person is available. This paper describes an effective algorithm for recognition and verification with one sample image per class. It uses two dimensional discrete wavelet transform (2D DWT) to extract features from images and hidden Markov model (HMM) was used for training, recognition and classification. It was tested with a subset of the AT&T database and up to 90% correct classification (Hit) and false acceptance rate (FAR) of 0.02% was achieved.
Multibiometric systems are expected to be more reliable than unimodal biometric systems for personal identification due to the presence of multiple, fairly independent pieces of evidence e.g. Unique Identification Project "Aadhaar" of Government of India. In this paper, we present a novel wavelet based technique to perform fusion at the feature level and score level by considering two biometric modalities, face and fingerprint. The results indicate that the proposed technique can lead to substantial improvement in multimodal matching performance. The proposed technique is simple because of no preprocessing of raw biometric traits as well as no feature and score normalization.
IRJET- A Plant Identification and Recommendation SystemIRJET Journal
This document describes a plant identification and recommendation system that uses image recognition techniques. The system takes an image of a leaf as input, preprocesses it by resizing, converting to grayscale, and extracting features. It then uses a convolutional neural network with the Inception-v3 model to identify the plant by comparing features to those in its database. Based on the identified plant, it recommends other plants that could grow in that location. The system is implemented as both a mobile app and web application to be accessible anywhere.
License plate recognition an insight to the proposed approach for plate local...Editor Jacotech
This document summarizes a journal article that proposes an approach for license plate localization and binarization in license plate recognition systems. The article describes the typical three-stage process of license plate recognition including localization, character segmentation, and character recognition. It then discusses challenges with existing localization approaches for Indian license plates due to variations in formats. The proposed approach exploits features like aspect ratio, texture, and color similarity to localize and extract license plates from images as a preprocessing step before character segmentation and recognition.
Cloud computing a services business application challengesEditor Jacotech
This document discusses challenges related to adopting Software as a Service (SaaS) business applications. It reviews literature that has identified key challenges such as data security, customization, and scalability. The document provides background on cloud computing models including Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). It also discusses characteristics of the SaaS cloud computing model and the business value it provides.
A mobile monitoring and alert sms system with remote configuration – a case s...Editor Jacotech
This document describes a mobile monitoring and alert SMS system for tracking children's locations using Android devices. It has the following key points:
1. The system allows parents to monitor children's locations through SMS messages without requiring an internet connection, keeping costs low. It supports single position requests, scheduled location updates, and geofenced alerts when a child leaves a specified range.
2. The system architecture includes an Android app on the child's device to track location via GPS and respond to SMS requests, and a separate parent app to send requests and view locations. The child app can be configured to only accept requests from authorized phone numbers for security.
3. Remote monitoring is performed by the parent app sending SMS commands
This document summarizes and compares various encryption algorithms for providing security in cloud computing environments. It first discusses key-policy attribute-based encryption (KP-ABE) which associates attributes with keys and policies with data. It also covers ciphertext-policy attribute-based encryption (CP-ABE) which associates policies with ciphertext and attributes with keys. The document then analyzes expressive KP-ABE and ciphertext-policy attribute set-based encryption (CP-ASBE) which uses hierarchical attribute sets. It concludes that improving previous work to leverage hierarchical attribute sets of users may enhance security and access control when utilizing cloud computing.
Design of airfoil using backpropagation training with mixed approachEditor Jacotech
The document describes a new algorithm for designing airfoils using neural networks. The algorithm uses a mixed training approach: it trains the output layer of the neural network using a cost function with linear and nonlinear error terms for faster convergence, while training the hidden layer using steepest descent. Results show the mixed approach converges much faster than traditional backpropagation or Levenberg-Marquardt algorithms alone. The algorithm more accurately predicts airfoil profiles with fewer training iterations.
Privacy in Location-Based Services using SP-Filtering in Hide and Seek Protoc...Editor Jacotech
This document summarizes a research paper about preserving privacy in location-based services. It discusses how location privacy is an important issue for location-based services. It describes an architecture used to implement privacy-preserving techniques at the service layer without interfering with existing services. It then covers various privacy-preserving techniques including obfuscation-based methods, SP-filtering protocols, and hide and seek protocols. It provides details on how obfuscation can be used to generalize user locations. It also explains how SP-filtering protocols work to calculate the minimum and maximum distances between generalized locations to determine proximity. Finally, it describes how hide and seek protocols can be combined with SP-filtering to more accurately determine proximity when locations may overlap
Statistical Analysis and Model Validation of Gompertz Model on different Real...Editor Jacotech
This document summarizes statistical analysis and model validation of the Gompertz model on different real data sets for reliability modeling. It presents the maximum likelihood estimation of parameters for the Gompertz model using the Newton-Raphson method. Goodness of fit tests including the Kolmogorov-Smirnov test and quantile-quantile plot are used to validate the Gompertz model on six different real data sets and determine which data sets provide the best fit for parameter estimation of the Gompertz model.
Modeling of solar array and analyze the current transientEditor Jacotech
Spacecraft bus voltage is regulated by power
conditioning unit using switching shunt voltage regulator having
solar array cells as the primary source of power. This source
switches between the bus loads and the shunt switch for fine
control of spacecraft bus voltage. The effect of solar array cell
capacitance [5][6] along with inductance and resistance of the
interface wires between solar cells and power conditioning
unit[1], generates damped sinusoidal currents superimposed on
the short circuit current of solar cell when shunted through
switch. The peak current stress on the shunt switch is to be
considered in the selection of shunt switch in power conditioning
unit. The analysis of current transients of shunt switch in PCU
considering actual spacecraft interface wire length by
illumination of solar panel (combination of series and parallel
solar cells) is difficult with hardware simulation. Software
simulation by modeling solar cell is carried out for a single string
(one parallel) in Pspice [6]. Since in spacecrafts number of
parallels and interface cable length are variable parameters the
analysis of current transients of shunt switch is carried out by
modeling solar array with the help of solar cell model[6] for the
actual spacecraft condition.
Performance analysis of aodv with the constraints ofEditor Jacotech
This document summarizes a research paper that analyzed the performance of the AODV routing protocol in wireless sensor networks under different terrain area sizes and pause times using the NS-3 simulator. The researchers found that packet delivery ratio remained nearly constant for small terrain areas but decreased for larger areas. Average throughput decreased with larger terrain areas, while average delay remained nearly constant for small areas but increased for larger ones. The paper concludes that AODV has better performance in networks with high mobility and size and is preferred for real-time traffic over other protocols like DSR and DSDV.
SECURITY CONCERN ON CLOUD BASED ON ATTRIBUTES: AN SURVEYEditor Jacotech
This document summarizes and compares various encryption algorithms for providing security in cloud computing environments. It first discusses key-policy attribute-based encryption (KP-ABE) which associates attributes with keys and policies with data. It also covers ciphertext-policy attribute-based encryption (CP-ABE) which associates policies with ciphertext and attributes with keys. The document then analyzes expressive KP-ABE and ciphertext-policy attribute set-based encryption (CP-ASBE) which uses hierarchical attribute sets. It concludes that improving previous work to leverage hierarchical attribute sets of users may enhance security and access control when utilizing cloud computing.
Technical study of automatic traffic control systemEditor Jacotech
The Technical study analyses existing and expected situation, defines critical localities on the basis of this analysis, which is necessary to solve and defines a framework for solution and its technical characteristics, respectively different alternatives of solutions. The article describes the current state of the ITS in the Delhi, NCR, the scope of the Technical study of urban ITS and an ideological proposal of the Traffic Control System (TCS) in Delhi, NCR. The urban ITS with respect to main goal – capacity increasing of existing urban road network and traffic congestion reducing – has been designed.
A survey of modified support vector machine using particle of swarm optimizat...Editor Jacotech
This document summarizes a research paper that proposes a modified support vector machine (MSVM) classification algorithm using particle swarm optimization (PSO) for data classification in data streams. It discusses how new evolving features and concept drift in data streams can decrease the performance of traditional SVM classifiers. The proposed MSVM-PSO technique uses PSO to optimize feature selection and control the evaluation of new evolving features. PSO works in two phases - dynamic population selection and optimization of new evolved features. The methodology and implementation of MSVM-PSO is explained along with experimental results on three datasets showing it improves classification performance over traditional SVM.
Designing geometric parameters of axisymmetrical cassegrain antenna and corru...Editor Jacotech
Early detection of faults occurring in three-phase induction motors can appreciably reduce the costs of maintenance, which could otherwise be too much costly to repair. Internal faults in three phase induction motors can result in significant performance degradation and eventual system failures. Artificial intelligence techniques have numerous advantages over conventional Model-based and Signal Processing fault diagnostic approaches; therefore, in this paper, a soft-computing system was studied through Neural Network Analysis to detect and diagnose the stator and rotor faults. The fault diagnostic system for three-phase induction motors samples the fault symptoms and then uses a Neural Network model to first train and then identify the fault which gives fast accurate diagnostics. This approach can also be extended to other applications.
Basic Concept of the Technical Study of the Automatic Traffic Congestion Cont...Editor Jacotech
This document summarizes a technical study analyzing an automatic traffic congestion control system for Delhi, NCR Region in India. The study analyzed the current state of intelligent transportation systems in the region and proposed a framework for an integrated urban traffic control system. The proposed system would consist of four main implementation domains: traffic information generation and acquisition, intelligent transportation systems for main roads, and intelligent transportation systems for urban agglomerations. For the urban areas, the study analyzed external traffic conditions and defined critical locations for 11 cities. It then proposed a traffic control system consisting of localized traffic control, vehicle detection, traffic monitoring cameras, and variable message signs to optimize traffic flow and reduce congestion across the region.
This document analyzes the use of social media like Facebook, Twitter, and blogs for small and medium scale businesses and discusses related security issues. It finds that social media provides an effective way for smaller businesses to do e-marketing and promotion. While useful for networking and branding, security must be maintained to protect personal and business information. Facebook is seen as the most effective platform for e-marketing due to its large user base and customizable advertising options.
Ant colony optimization based routing algorithm in various wireless sensor ne...Editor Jacotech
Wireless Sensor Network has several issues and challenges due to limited battery backup, limited computation capability, and limited computation capability. These issues and challenges must be taken care while designing the algorithms to increase the Network lifetime of WSN. Routing, the act of moving information across an internet world from a source to a destination is one of the vital issue associated with Wireless Sensor Network. The Ant Colony Optimization (ACO) algorithm is a probabilistic technique for solving computational problems that can be used to find optimal paths through graphs. The short route will be increasingly enhanced therefore become more attractive. The foraging behavior and optimal route finding capability of ants can be the inspiration for ACO based algorithm in WSN. The nature of ants is to wander randomly in search of food from their nest. While moving, ants lay down a pheromone trail on the ground. This chemical pheromone has the ability to evaporate with the time. Ants have the ability to smell pheromone. When selecting their path, they tend to select, probably the paths that has strong pheromone concentrations. As soon as an ant finds a food source, carries some of it back to the nest. While returning, the quantity of chemical pheromone that an ant lay down on the ground may depend on the quantity and quality of the food. The pheromone trails will lead other ants towards the food source. The path which has the strongest pheromone concentration is followed by the ant which is the shortest paths between their nest and food source. This paper surveys the ACO based routing in various Networking domains like Wireless Sensor Networks and Mobile Ad Hoc Networks.
Survey on energy efficiency in wireless sensor network using mac protocol wit...Editor Jacotech
Dynamic feature evaluation and concept evaluation is major challenging task in the field of data classification. The continuity of data induced a new feature during classification process, but the classification process is predefined task for assigning data into class. Data comes into multiple feature sub-set format into infinite length. The infinite length not decided the how many class are assigned. Support vector machine is well recognized method for data classification. For the process of support vector machine evaluation of new feature during classification is major problem. The problem of feature evaluation decreases the performance of Support Vector Machine (SVM). For the improvement of support vector machine, particle of swarm optimization technique is used. Particle of swarm optimization controls the dynamic feature evaluation process and decreases the possibility of confusion in selection of class and increase the classification ratio of support vector machine. Particle of swarm optimization work in two phases one used as dynamic population selection and another are used for optimization process of evolved new feature.
A mobile monitoring and alert sms system with remote configuration – a case s...Editor Jacotech
One of the parent´s main concerns nowadays it to know their children´s whereabouts. Some applications exist to address this issue and most of them rely on internet connection which makes the solution expensive. In this paper we present a low cost solution, based on SMS, and with the ability to remotely configure the child monitoring process. We also present the architecture and the full flowchart of the child application whenever a SMS is received. This case study uses Android and the more recent location API – the Fused Location Provider. For obvious reasons, the security issue has been a concern, which resulted in a configuration module in the child application to specify authorized senders
A Comparative Study on Identical Face Classification using Machine LearningIRJET Journal
This document presents research on classifying identical faces using machine learning techniques like support vector machines (SVM). The researchers aim to develop an accurate technique for identifying the same faces from facial photographs. They discuss using SVM classifiers and combining multiple SVM classifiers using plurality voting. They compare the SVM classification approach to standard identical face classification methods. The document also provides background on machine learning and supervised learning techniques like logistic regression, SVM, and random forest classifiers. It discusses related work applying SVM, neural networks, and other methods to tasks like facial expression classification, emotion classification, age and gender recognition.
IRJET- Class Attendance using Face Detection and Recognition with OPENCVIRJET Journal
This document describes a system to automate class attendance using face detection and recognition with OpenCV. The system uses the Viola-Jones algorithm for face detection and linear binary pattern histograms for face recognition. Detected faces are converted to grayscale images for better accuracy. The system trains on positive images of faces and negative images without faces to build a classifier. It then detects faces in class and recognizes students by matching features to a stored database, updating attendance and notifying administrators. The proposed system aims to reduce time spent on manual attendance and increase accuracy by automating the process through computer vision techniques.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The document discusses challenges and approaches for facial emotion recognition. It aims to develop a model-based approach for real-time driver emotion recognition on an embedded platform using parallel processing. Model-based approaches can overcome issues like illumination and pose variations. The document reviews several state-of-the-art methods and discusses challenges like occlusion, lighting distortions, and complex backgrounds. It describes exploring both 2D and 3D techniques for facial feature extraction and expression recognition.
Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.
Facial expression recongnition Techniques, Database and Classifiers Rupinder Saini
This document discusses various techniques for facial expression recognition including eigenface approach, principal component analysis (PCA), Gabor wavelets, PCA with singular value decomposition, independent component analysis with PCA, local Gabor binary patterns, and support vector machines. It describes databases commonly used for facial expression recognition research and classifiers such as Euclidean distance, backpropagation neural networks, PCA, and linear discriminant analysis. The document concludes that combining multiple techniques can achieve more accurate facial expression recognition compared to individual techniques alone by extracting relevant features and evaluating results.
Human face detection is a significant problem of
image processing and is usually a first step for face
recognition and visual surveillance. This paper presents the
details of face detection approach that is implemented to
achieve accurate face detection in group color images which
are based on facial feature and Support Vector Machine. In
the first step, the proposed approach quickly separates skin
color regions from the background and from non-skin color
regions using YCbCr color space transformation. After the
detection of skin regions, the images are processed with,
wavelet transforms (WT) and discrete cosine transforms
(DCT) as a result of which the 30×30 pixel sub images are
found. These sub images are then assigned to SVM classifier
as an input. The SVM is used to classify non-face regions from
the remaining regions more accurately, that are obtained
from previous steps and having big difference between faces
regions and non-faces regions. The experimental results on
different types of group color images show that this approach
improves the detection speed and minimizes the false
detection rate in less time and detects faces in different color
images.
This document presents a hybrid framework for facial expression recognition that uses SVD, PCA, and SURF. It extracts features using PCA with SVD, classifies expressions with an SVM classifier, and performs emotion detection with regression and SURF features. The framework achieves 98.79% accuracy and 67.79% average recognition on a database of 50 images with 5 expressions. It provides a concise facial expression recognition system using a combination of dimensionality reduction, classification, and feature detection techniques.
This document discusses face detection and recognition techniques using MATLAB. It begins with an abstract describing face detection as determining the location and size of faces in images and ignoring other objects. It then discusses implementing an algorithm to recognize faces from images in near real-time by calculating the difference between an input face and the average of faces in a training set. The document then provides details on various face recognition methods, the 5 step process of facial recognition, benefits and applications, and concludes that recent algorithms are much more accurate than older ones.
This document describes research on an improved approach for eigenface recognition. The key points are:
- Eigenface recognition is a common method for face recognition that uses principal component analysis to define a face space from a training set of faces.
- The proposed improved eigenface approach aims to achieve better accuracy than existing approaches. It uses MATLAB to design and develop the algorithm.
- The methodology involves loading training images, constructing the face space using mean images and eigenvectors, and classifying new images by comparing them to existing face classes.
- Experimental results show the improved eigenface approach provides better performance than existing methods for face recognition.
A Novel Approach of Fuzzy Based Semi-Automatic Annotation for Similar Facial ...ijsrd.com
This paper proposes a semi-automatic approach for annotating similar facial images that are often weakly labeled with duplicate, noisy, or incomplete names. It uses an unsupervised label refinement (ULR) algorithm with fuzzy clustering to improve the labels. The ULR algorithm refines the labels through multiple iterations using machine learning techniques. It also uses a parallel computation framework to solve very large problems efficiently. Evaluation on a dataset with introduced noise shows the proposed optimized fuzzy ULR approach outperforms other ULR algorithms in refining the labels.
Face Recognition Using Simplified Fuzzy Artmapsipij
Face recognition has become one of the most active research areas of pattern recognition since the early 1990s. This project thesis proposes a novel face recognition method based on Simplified Fuzzy ARTMAP (SFAM). For extracting features to be used for classification, combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) is used. This is for improving the capability of LDA and PCA when used alone.PCA reduces the dimensionality of input face images while LDA extracts the features that help the classifier to classify the input face images. The classifier employed was SFAM. Experiment is conducted on ORL, Yale and Indian Face Database and results demonstrate SFAM’s efficiency as a recognizer. The training time of SFAM is negligible. SFAM has the added advantage that the network is adaptive, that is, during testing phase if the network comes across a new face that it is not trained for; the network identifies this to be a new face and also learns this new face. Thus SFAM can be used in applications where database needs to be updated frequently. SFAM thus proves itself to be an efficient recognizer when a speedy, accurate and adaptive Face Recognition System is required.
This document discusses face recognition using the PCA algorithm. It begins with an introduction to face recognition and its challenges. It then provides background on face recognition techniques, including PCA. The document outlines an improved PCA (IPCA) algorithm that aims to address issues like orientation and lighting variations. It presents results of the IPCA algorithm on two test cases, showing it can accurately recognize faces even at 90 degree orientations. The document discusses advantages of face recognition but also limitations like sensitivity to expressions, lighting and angle. It raises privacy concerns about widespread use of facial recognition technology.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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Brain Tumor Classification using Support Vector MachineIRJET Journal
1) The document presents a method for classifying brain tumors as cancerous or non-cancerous using support vector machines (SVM) and image processing techniques.
2) MRI images of brain tumors are preprocessed, features are extracted, and feature vectors are generated before being classified by an SVM classifier trained on labeled tumor data.
3) The SVM model achieves high accuracy in classifying tumors, which is evaluated using measures like true positives, true negatives, false positives and false negatives. This automated classification could help in diagnosis and treatment of brain tumors.
Attendance System using Facial RecognitionIRJET Journal
This document presents a study on developing an automated attendance system using facial recognition. The system uses deep learning algorithms for face detection and recognition to mark student attendance automatically. It compares Viola-Jones and CNN-based face detection, finding that CNN performs better as it can detect faces from any angle with fewer discrepancies. For recognition, it uses dlib's CNN model to extract 128-dimensional encodings from detected faces and compares them to a trained database to identify students and record attendance in an Excel sheet. Testing showed the deep learning-based system achieved 85-95% accuracy in normal conditions and 70-80% in dim environments, providing a more efficient alternative to manual attendance marking.
This document presents a method for real-time facial expression analysis using principal component analysis (PCA). The method involves detecting faces, extracting expression features from the eye and mouth regions, applying PCA to extract texture features, and using a support vector machine classifier to classify expressions. The proposed approach was tested on a database of facial images with expressions categorized as happy, angry, disgust, sad, or neutral. PCA was used to select the most relevant eigenfaces and reduce the dimensionality of the feature space for more efficient classification of expressions in real-time.
The document presents a study that implemented segmentation and classification techniques for mammogram images to detect breast cancer malignancy. It used Gray Level Difference Method (GLDM) and Gabor texture feature extraction methods with Support Vector Machine (SVM) and K-Nearest Neighbors (K-NN) classifiers. The results showed that GLDM features with SVM achieved the best classification accuracy of 95.83%, outperforming the other combinations. The study concluded the GLDM and SVM approach provided the most effective classification of mammogram images.
AN EFFICIENT FACE RECOGNITION EMPLOYING SVM AND BU-LDPIRJET Journal
The document presents a study on an efficient face recognition method employing support vector machines (SVM) and biomimetic uncorrelated local difference projection (BU-LDP). The study proposes using BU-LDP, which is based on uncorrelated local projection but uses a different neighborhood coefficient calculation approach inspired by human perception. Experimental results on several datasets show that BU-LDP and its kernel variant KBU-LDP outperform state-of-the-art methods for face recognition. Future work will focus on addressing the "one sample problem" and applying the approach to unlabeled data.
Performance of Wideband Mobile Channel with Perfect Synchronism BPSK vs QPSK ...Editor Jacotech
Direct-sequence code-division multiple access (DS-CDMA) is
currently the subject of much research as it is a promising
multiple access capability for third and fourth generations
mobile communication systems. The synchronous DS-CDMA
system is well known for eliminating the effects of multiple
access interference (MAI) which limits the capacity and
degrades the BER performance of the system. In this paper,
we investigate the bit error rate (BER) performance of a
synchronous DS-CDMA system over a wideband mobile
radio channel. The BER performance is affected by the
difference in path length ΔL and the number of arriving
signals N. Furthermore, the effect of these parameters is
examined on the synchronous DS-CDMA system for different
users’ number as well as different processing gain Gp. In this
environment and under the above conditions the performances
of the BPSK (Binary Phase Shift Keying) and the QPSK
(Quadrature Phase Shift Keying) modulations are compared.
The promising simulation results showed the possibility of
applying this system to the wideband mobile radio channel.
MOVIE RATING PREDICTION BASED ON TWITTER SENTIMENT ANALYSISEditor Jacotech
With microblogging platforms such as Twitter generating
huge amounts of textual data every day, the possibilities of
knowledge discovery through Twitter data becomes
increasingly relevant. Similar to the public voting mechanism
on websites such as the Internet Movie Database (IMDb) that
aggregates movies ratings, Twitter content contains
reflections of public opinion about movies. This study aims to
explore the use of Twitter content as textual data for
predicting the movie rating. In this study, we extract number
of tweets and compiled to predict the rating scores of newly
released movies. Predictions were done with the algorithms,
exploring the tweet polarity. In addition, this study explores
the use of several different kinds of tweet classification
Algorithm and movie rating algorithm. Results show that
movie rating developed by our application is compared to
IMDB and Rotten Tomatoes.
Non integer order controller based robust performance analysis of a conical t...Editor Jacotech
The design of robust controller for any non linear process is a
challenging task because of the presence of various types of
uncertainties. In this paper, various design methods of robust
PID controller for the level control of conical tank are
discussed. Uncertainties are of different types, among that
structured uncertainty of 30% is introduced to the nominal
plant for analysing the robustness. As a first step, the control
of level is done by using conventional integer order controller
for both nominal and uncertain system. Then, the control is
done by means of Fractional Order Proportional Integral
Derivative (FOPID) controller for achieving robustness. With
the help of time series parameters, a comparison is made
between conventional PID and FOPID with respect to the
simulated output using MATLAB and also analyzed the
robustness.
FACTORS CAUSING STRESS AMONG FEMALE DOCTORS (A COMPARATIVE STUDY BETWEEN SELE...Editor Jacotech
This document summarizes a research study that examined factors causing stress among female doctors working in public and private sector hospitals in India. The study aimed to identify whether there were associations between hospital sector (public or private) and 12 different stress measures among 300 female doctors. A survey was administered to collect data. Chi-square tests found statistically significant associations (p < 0.05) between hospital sector and 11 of the 12 stress measures, including stress due to workload, working conditions, physical exertion, emotional exhaustion, job security, organizational support, work-family conflict, family adjustment, task demands, patient expectations, and working hours. Only the association between sector and stress due to psychosomatic problems was not statistically significant. The results indicate
ANALYSIS AND DESIGN OF MULTIPLE WATERMARKING IN A VIDEO FOR AUTHENTICATION AN...Editor Jacotech
Watermarking technique be employ instance & for a second time for
validation and protection of digital data (images, video and audio
files, digital repositories and libraries, web publishing). It is helpful
to copyright protection and illegal copying of digital data like video
frames and making digital data more robust and imperceptible. With
the advent of internet, creation and delivery of digital data has grown
many fold. In that Scenario has to need a technique for transferring
digital data securely without changing their originality and
robustness. In this paper proposed a plan of latest watermarking
method which involves inserting and adding two or more digital data
or pictures in a single video frame for the principle of protection and
replicate the similar procedure for N no video frames for
authentication of entire digital video. After that digital video is
encrypted and decrypted by using motion vector bit-xor encryption
and decryption technique.
The Impact of Line Resistance on the Performance of Controllable Series Compe...Editor Jacotech
In recent years controllable FACTS devices are increasingly
integrated into the transmission system. FACTS devices that
provide series control such as Controllable Series Compensator
(CSC) has significant effect on the voltage stability of Electric
Power system. In this work impact of line resistance on the
performance of CSC in a single-load infinitive-bus (SLIB)
model is investigated. The proposed framework is applied to
SLIB model and obtained results demonstrates that line
resistance has considerable effect on voltage stability limits and
performance of CSC.
Security Strength Evaluation of Some Chaos Based Substitution-BoxesEditor Jacotech
Recently, handful amount of S-boxes, using the various
methods such as affine transformations, gray coding,
optimization, chaotic systems, etc, have been suggested. It is
prudent to use cryptographically strong S-boxes for the design
of powerful ciphers. In this paper, we sampled some widely
used 8×8 S-boxes which are recently synthesized and security
analysis and evaluation is executed to uncover the best
candidate(s). The performance analysis is exercised against
the crucial measures like nonlinearity, linear approximation
probability, algebraic immunity, algebraic complexity,
differential uniformity. These parameters are custom selected
because their scores decide the security strength against
cryptographic assaults like linear cryptanalysis, algebraic
attacks, and differential cryptanalysis. The anticipated
analysis in this work facilitates the cryptographers, designers,
researchers to choose suitable candidate decided over many
parameters and can be engaged in modern block encryption
systems that solely rely on 8×8 S-box. Moreover, the analysis
assists in articulating efficient S-boxes and to evaluate the
attacks resistivity of their S-boxes.
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1. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No.1 Issue No. 1, August 2013
Analysis of Classification Techniques based on SVM for Face
Recognition
By
Lucknesh Kumar,Sarita Bharti
MNNIT,Allahbad,India
GCET,Greater Noida
Lucknesh.mnnit@gmail.com, bhartisarita31@gmail.com
ABSTRACT
SVM and ISVM are the most prominent technique for
excellent learning and classification in the field of machine
learning such as face detection and recognition, handwriting
automatic identification and automatic text categorization,
video tracking, Number plate recognition, Traffic Control etc.
Nowadays, Face recognition is a challenging task in the field
of computer vision. Motive of face recognition is to compare
between the Given face database with the input face image
and then declare a decision that identifies to whom the input
image class belongs to or doesn’t belong to the face
database.In this analysis we not only study and also compare
the sophisticated classification technique i.e. SVM and ISVM
for face recognition along with its pros and cons.
Keywords
Face Recognition, Machine Learning, Computer Vision,
Support Vector Machine, Incremental Support Vector
Machine, Classification
1. INTRODUCTION
Today, Face recognition is the huge research area of machine
learning and computer vision. Automated crowd surveillance,
access control, Mugshot identification (e.g., for issuing driver
licenses), face reconstruction, design of human computer
interface (HCI), multimedia communication (e.g., generation
of synthetic faces) are the large number of commercial,
security, and forensic applications requiring the use of face
recognition technologies. [22]
Face recognition can be done by three basic techniques:
structural matching method based on the characteristics,
whole matching method and combination method. Geometric
characteristics of the face, such as the location ,size, relations
of eyes, nose,chin and so on, are used to represent the face in
structural matching method; whereas in whole matching
method, the gray image of whole face acts as input to train
and test the classifier, such as the wavelet-based.
for a preliminary identification, and then local Features for
further identification [10].
In an incremental world, We have a high resolution
camera to capture facial images which have lain in the high
dimensional space it causes a serious problem in accurate face
recognition. So we mapped these high-dimensional spaces in
lower-dimensional
space,
conventional
classification
algorithms can then be applied[11].
Basic Concept Of SVM
Support vector machines (SVMs) are the supervised
learning and basic algorithm mostly for classification and
pattern recognition based on guaranteed risk bounds of
statistical learning theory. This support vector machine theory
is developed by Vladimir Vapnik & his team in 1995 at AT&
Bell Laboratories, and the principle is based on structural risk
minimization, so it has very good generalization ability [23]
The basic principle of SVM is construct a hyperplane as
the decision plane which is binary class with the largest
margin to find the optimal hyperplane making expected errors
minimized to the unknown test data, while the location of the
separating hyperplane is specified via only data that lie close
to the decision boundary between the two classes, which are
support vectors. [9]
By using support vector machine classify all window patterns
and if the class matches a face then make a square around the
face in the output image.
SVM is fast and robust learning machine for binary
classification, it has demonstrated good empirical results. It
offers to detect faces in various poses and orientations. On the
other hand, SVM suffers some demerits i.e.It is usually
needed to look for the space and scale and It requires lots of
positive and negative examples.
Elastic Matching, the principal component analysis and
so on; combination method is a combination of the two
former methods, usually the overall characteristics are used
1
2. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No.1 Issue No. 1, August 2013
2.
Three-dimensional (3D) facial data have been
exploited as a means to improve the effectiveness of
face recognition systems.
Cons:
1. Acquisition of 3D facial data of a person can be
accomplished only in controlled environments and requires
the person to stay still in front of a 3D scanning device for a
time that ranges from some seconds up to a few minutes [1].
NonFaces
2.
Facial Expression Classification from Gabor features
using SVM :
Faces
Figure1.1: SVM separate the face and non-face by geometrical
interpretation. The patterns are real support vectors
obtained after training the system [23]
Basic Concept of ISVM
Incremental learning means On-line learning process
which was proposed by [Syed, 1999] to solve the problem of
sequentially real time data updating[24]. The algorithm, first,
train training data set and obtain classifier and all support
vectors; then we obtain new training data set through merging
support vectors and new adding data set; finally, we train new
training data set [14].
Incremental learning can be implemented by to train the
classifier using batches of data subsets, means at a time only
one subset of the database will be trained after that result will
be combined, we can also say that this technique follows
divide and conquer using this method, the learning results is
“incremental” combined and deposited. [15]
2. RELATED WORK
There are the some classification techniques for face
recognition using SVM as :
Author analyzed facial expressions using Gabor features,
which aims to reduce the computational complexity. For each
fixed scale and orientation, a set of Gabor faces are obtained.
The Gabor features extracted from different blocks of Gabor
faces are used for further analysis. Support Vector Machine is
used to classify different expressions [18].
Features based on Gabor filters have been used in image
processing due to their powerful properties. The main
characteristics of wavelet are the possibility to provide a multi
resolution analysis of the image in the form of coefficient
matrices. These are used to extract facial appearance changes
as a set of multiscale and multi orientation coefficients. Gabor
filter is shown to be robust against noise and changes in
illumination. Gabor kernels are characterized as localized,
orientation selective, and frequency selective. The Gabor
wavelet representation of images allows description of spatial
frequency structure in the image while preserving information
about spatial relations[18].
Images are divided in to 5 blocks of 28 x 28 sizes. Mean
and standard deviation are computed for each sub block. This
is considered as feature vectors to an SVM classifier, which is
used to discriminate different types of expressions. Initially
one expression group is selected. All the images under this
group are classified as +1 and others as -1.This iteration
process continues until all the expression groups are classified
properly[18].
Pros:
1. Face Recognition By SVM’s Classification Of 2D And
3D Radial Geodesics :
In this paper, Support Vector Machines (SVMs) are used
to perform face recognition using 2D- and 3D radial
geodesics. Motive of this technique is to analyze facial
expression of 2D and 3D face images using radial geodesic
distances (RGDs) computed with respect to a reference point
of the face (i.e., the nose tip).
Matching between 2D- and 3D-RGDs results into feature
vectors which are classified by a set of Support Vector
Machines (SVMs). Since the feature vectors lay in a highdimensional space, dimensionality reduction methods are
applied before SVMs classification[1].
Pros:
1.
2D and 3D images feature extraction directly
compare.
1.
It reduces the complexity
2.
The Gabor wavelet representation of images allows
description of spatial frequency structure in the
image while preserving information about spatial
relations. This method improves both the processing
speed and efficiency
Cons:
1.
3.
Evaluating filters to convolve the face image is
quite time consuming.
A Heuristic Algorithm to Incremental SVM Learning:
Most incremental learning algorithm are based on
improving SVM algorithm and collecting more useful data as
support vectors, while some are based on concept drift.
Heuristic algorithm is the first case[19].
Considering there are series of hyperplanes ψi closing up
to the optimal hyperplane gradually. During this gradual
change, the difference between any two partitions of data set
2
3. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No.1 Issue No. 1, August 2013
is slowing down gradually, ideally even to zero when
assuming the optimal hyperplane can, separate the data
completely and correctly [19]. The main idea of this
consideration is that data points in difference are much closer
to the optimal hyperplane, and a series of hyperplanes are
gradually closer to the optimal one with the data points in
partition difference set getting less and less, until less than a
given .
Pros:
1.
Based on improving SVM algorithm and collecting
more useful data as support vectors.
Cons :
1.
The difference between any two partitions of data
set is slowing down gradually.
4. A Divisional Incremental Training Algorithm of SVM :
In this paper a divisional incremental SVM training
algorithm was proposed to improve the classification. In this
method actions are taken not only to support vectors but also
these data between the merges of the two classes in each step
of batch incremental learning model. We can easily
summarize this method as: Firstly, classify the current/new
new data set with fixed hyperplane to collect the data that are
between the merges of the two classes. Then divide current
training dataset into two or there smaller subsets and combine
them with new coming dataset to collect support vectors
respectively. After that combine the data collected in former
steps to construct new hyperplane, until the classification
precision is satisfying [2].
Pros:
1.
Cons :
1.
Incremental or online data set handle more easily in
other words we can say dataset classification is done
accurately.
It is known that the key to construct an optimal
hyperplane is the support vectors and in batch
incremental learning framework, a support vectors
would not contribute its effect to the optimal
hyperplane after several steps learning.
5. A Training Algorithm of Incremental Support Vector
Machine with Recombining Method :
Proposed incremental SVM learning algorithm with
batch model divides the training datasets in batches (subsets)
that may fit into memory suitably. For each new batch of data,
a SVM is trained on the new data and the support vectors
from the previous learning step. It is proved that separating
hyperplane is subject to support vectors of training dataset, so
batch model learning may not collect all support vectors
which are needed for incremental learning algorithm because
the distribution state of batches of training datasets are usually
unknown[20].
According to the distribution characteristic of support
vectors, it is known that the data located between the marges
of the two classes are not classifiable correctly and easily.
Therefore, less of these data exist, higher of the classification
accuracy may be obtained. Meanwhile, the impact of the
distribution differences between new coming data and the
history training data can not be ignored in various practical
applications. Taking the above into consideration, the
implementation process of batch learning model should be
improved. Since the support vectors are key factors to
construct a hyperplane, the improvement to be done is collect
more potential data as support vectors. To be more specific, in
every step of batch learning model, some actions are taken not
only to support vectors but also the data between the marges
of the two classes. That is, the training dataset and the new
coming dataset are divided into smaller-sized groups and then
recombined in a crossed way to obtain several independent
sets of support vectors. At last, combine these independent
sets of support vectors and train the final hyperplane as the
output of each step in batch learning model[20].
Pros: 1. Divides the training datasets in subsets that may
fit into memory suitably.
Cons : 1. The distribution state of batches of training
datasets are usually unknown.
6. Investigation of Feature Dimension Reduction based
DCT/SVM for Face Recognition:
In this technique author was removed Some redundant
information by truncating the DCT coefficients so that the
dimensionality of the coefficient vectors can be reduced. After
that detected face image is divided into blocks of 8 x 8 pixels
size then On each block DCT is performed and 64 DCT
coefficients covering all the spatial frequency components of
the block are extracted. The obtained DCT coefficients are
ordered using the zigzag scanning where the coefficients
placed next to each other tend to be of similar magnitude, thus
making the row of coefficients more suitable for
generalization[21].
Later recognition is performed by using SVM classifier.
The ‘one-against-one’ strategy is used to train the SVM
classifiers. Selected feature vectors are then fed into multiclass SVM to classify the input data as a face ID or not[21].
When the powerful features of the SVM, such as margin
maximization and kernel substitution for classifying data in a
high dimensional kernel space, are combined with the low
dimensional DCT feature vector as described, a good
compromise between the computational efficiency and
performance accuracy is obtaind.
Pros:
1.Simplify the computational complexity by truncating
the DCT coefficients.
Cons:
1.Statistical information and feature information are
essential for human face recognition
7.
Face Recognition Based on 2DLDA and Support
Vector Machine :
In this paper author give a comparison of LDA ND
2DLDA methods. In LDA, a vector representation is used,
3
4. Journal of Advanced Computing and Communication Technologies (ISSN: 2347 - 2804)
Volume No.1 Issue No. 1, August 2013
while in 2DLDA, a matrix representation is used, and in face
recognition, the performance of 2DLDA is higher than that of
LDA. this is a hybrid technique, in this, using wavelet
transform firstly original face images are decomposed into
high-frequency and low-frequency components. The highfrequency components are ignored, while the low-frequency
components can be obtained. Then, the liner discriminant
features are extracted by 2DLDA, and SVM is selected to
perform face recognition. The idea of SVM is that through a
nonlinear mapping the samples are converted into a higher
dimensional space, and then the optimal separating
hyperplane is solved[3].
Pros:
1.The SVM searches the optimal separating hyperplane
which separates the largest possible fraction of samples of the
same class on the same side.
oriented at 6 different angles and two low-pass sub-images.
By this means, we connect the 8 sub-images to a feature
vector. Then, we use a principal component analysis
technique to reduce the extracted feature dimensions. Finally,
support vector machine is used as the classifier to complete
face recognition. 2-D DTCWT has the approximate shiftinvariant property and good directional selectivity. It can
obtain the optimal orientation in the space and frequency
field. So, in this paper, the facial features are extracted The
realization method is described as following: [17]
Pros:
1. This approach achieves a higher recognition rate.
Cons :
1.
2.
Cons :
1.Maximizes the distance from either class to the
hyperplane.
8.
Fast SVM Incremental
Clustering Algorithm :
Learning
Based
on
An incremental SVM learning algorithm is proposed If
memory size is less to fit the data, incompleteness of data set
or in real time data set online learning is used. To solve these
two problems, the This technique presents a new incremental
learning algorithm combined SVM with clustering
algorithm[5].
In this algorithm, firstly clustering algorithm is applied
and found the cluster after that new training data set using
centers of clusters is constructed. Then new training data set is
trained with FSVM and support vectors are obtained. There
are two strategies to deal with new adding data set as:
(i)
one is to add new adding samples into the support
vector set that is get in the first step using
clustering algorithm;
3. CONCLUSION
SVM is the most important machine learning technique for
the pattern recognition, in this area research is going on to
improve of Qos (quality of service).In this paper we have
presented a survey of face recognition based on SVM. It also
describes how SVM technique is helpful for recognition of
faces and how to improve recognition rate using SVM. The
performance, concepts along with pros and cons of SVM
techniques for face recognition are summarized in this paper.
Performance analysis of various SVM techniques is discussed
in the table below.
S.No.
Techniques
Database
Recognition
Rate
1.
RGD + SVM
Image
86.9%
2.
Gabor + SVM
Image
90%
Text
95.0%
Text
95.46%
Text
96.18%
(ii) only to add samples that contrary to KKT condition
using UC algorithm and thrown away the
samples that are satisfied with KKT condition.
3.
In this technique, samples point near classification
hyperplane in detailed are analyzed.[5]
4.
Heuristic algo. +
ISVM
Divisional algo.+
ISVM
Pros:
1.It not only decreases the training time and predicting
time but also improves the classification accuracy rate[5].
it depends on the frequency characteristics of the
image to select the suitable frequency band.
It is also unpractical to realize the expansion of
complete gabor wavelet transform because it need a
great number of filter.
5.
ISVM +
Recombining
Cons :
1.This technique sometimes proves to be time consuming
when samples point near classification hyperplane are
analyzed in detail.
6.
DCT/SVM
Image
96.25%
7.
2DLDA + SVM
Image
96.67%
9.
8.
Clustering algo. +
Benchmark
96.92%
Image
99.5%
Combination Of Dual-Tree Complex Wavelet And
SVM For Face Recognition:
In this paper author was used DTCWT to extract the
human face features by 2-D dual-tree complex wavelet
transform. In this technique firstly each face image
decompose into six band-pass sub-images that are strongly
ISVM
9.
DTCW + SVM
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Volume No.1 Issue No. 1, August 2013
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