International Journal of Computer Science and Information Security (IJCSIS), Vol 14, No 10, October 2016 Using 3G Dongle, Recognition and Reporting based on Pervasive Computing Sonali Goyal Department of Computer Science and Engineering Haryana Engineering College, Jagadhri, Haryana, India Email- sonaliguglani21@gmail.com Neera Batra Department of Computer Science and Engineering Maharishi Markandeshwar University, Mullana, Haryana, India Email- batraneera1@gmail.com Abstract-Biometric systems are the safety systems which are based on physical, biological and the humans behavioural To find any face in a given image is the main purpose of face detection and for detecting faces, face detection technique is used Automatic face recognition has become a highly active research area and health monitoring in automatic way is an approach which is used to provide important information of human beings to their family members An integrated approach of PCA with ICA is used for feature extraction and with this combination; Neural Network is used as a classifier We have defined four categories in this so after recognizing an unknown face; we are sending messages to cell phones of those members of category from which it belongs and also to their family members by using 3G dongle Implementation part is done by using MATLAB and recognition rate is to be calculated Keywords – Pervasive Computing, Biometric Syystem, Face Recognition, Principle Component Analysis, Independent Component Analysis, Feed Forward Neural Network, Recognition Rate, 3G Dongle I INTRODUCTION To access the information and software applications anytime and anywhere, Pervasive Computing is used In contrast to Desktop Computing, this computing can occur using any type of device, in any location and in any format Pervasive Computing is a technology used for making computers so common that users are not even aware of their physical presence Supported self-care and services for home are the upcoming models of care Advances in sensors and network technologies have made this possible A smart home is equipped with smart technologies provides services that increase human way of living by providing its services i.e safety, security etc That allows elder person to maintain living independently and securely in their homes The care of elderly could be enhanced through remote monitoring system, sensor technologies and communication systems A form of home automation focuses on making it possible for elderly and individual people to remain at home safe and comfortable There are different types of face recognition algorithms which are used to recognize the face A hybrid or integrated combination of Principal Component Analysis with Independent Component Analysis with Feed Forward Neural Network is used for obtaining the desired results Hybrid combination of PCA and ICA is used for feature extraction and Feed Forward Neural Network is used for face recognition The development of PCA is found by Eigen Vectors and with the help of Eigen Vectors, features of given input are considered that are called as principle components Its advantage is dimensionality reduction process which includes the compression of data but there is no information is lost ICA is a technique which is very useful and used in systems involving multivariable data [8] A Feed-Forward Network is a simple neural network in which three layers are used first is input layer second is hidden layer and last one is output layer [14] We have taken four categories friend, family, neighbour and unknown If person is recognized at door it will check from which category it belongs, shows the type of category and then send message on mobile phone to all the person of that category Suppose a person is recognized as family member then the message will be send to all the family members who are registered 907 https://sites.google.com/site/ijcsis/ ISSN 1947-5500 International Journal of Computer Science and Information Security (IJCSIS), Vol 14, No 10, October 2016 Fig Face Detection System I.1 Pre-processing- Database containing front view and side view face images taken in 512 *512 pixels I.2 Feature Extraction- It is defined as the process of extracting useful with similar information through a face image [2] Meaningful and useful representation is derived for all the images by using a concept of converting the data from high dimensional input space to a lower dimensional space [13].PCA and ICA algorithms are used for this purpose I.3 Classifier- As a classifier, we use Feed Forward Neural Network for proposed method as it gives more accurate results in comparison with individual methods All the steps are implemented using MATLAB.MATLAB is a language having high performance used for technical computing and also used to generate computations in a way to use easily where mathematical notation is used to express all the problems and solutions While using MATLAB in face recognition, it stores the images in matrix form and then use PCA + ICA algorithm for feature extraction Description for rest of the paper is: In section II, Proposed hybrid algorithm is defined Results of Experiments are shown in section III and last part is Conclusion and Future Scope in section IV II PROPOSED HYBRID ALGORITHM Work already done in Face recognition system uses a combination of single feature extraction technique with classifier but work which is proposed now consists of four steps: Training set Pre-Processing Feature Extractor PCA Feature Extractor ICA Classifier Classifier Fusion Classified as ‘known’ or ‘unknown’ and its category Fig Hybrid Face recognition system 908 https://sites.google.com/site/ijcsis/ ISSN 1947-5500 International Journal of Computer Science and Information Security (IJCSIS), Vol 14, No 10, October 2016 Using 3G Dongle, Recognition and Reporting based on Pervasive Computing In first step, we take training set and pre-processing in which images are normalized by different methods In second step, Independent features have been extracted by using two face recognition algorithms PCA and ICA In third step, Feed Forward Neural Network is used for classification which classifies a given input image In fourth step, Combine all the outputs of each neural network and classifies input image which defines the category of each input image Steps for this hybrid algorithm are• • • • • To establish Training set Apply mark features on training set to collect eye and mouth position Apply align faces to scale the face images in pgm format All the images are stored in matrix form named as C Apply PCA functionTo centre the data-Every training image is centred by subtracting the mean image from each of the training images To calculate Co-variance matrix- Centred images in the matrix ‘A’ is multiplied with its transpose to form a Co-variance matrix L L= A * A’ To calculate Eigen vectors [V, D]= Eig[1/(P-1) * (B’ * B)) • Apply ICA algorithm- from eigenvectors , ICA is calculated with help of filtering and learning Centering – M= E{x} where x represents a zero mean variable Whitening-Converts observed vector x in linear way so that a new vector x’ is obtained which is white E{x * x’}= I LearningXx=inv(wz) * x To check output co-variance Cov(xx’) Where uu= w * wz * xx Setting up of threshold value and show result III EXPERIMENTS AND RESULT Integration of Principal Component Analysis with Independent Component Analysis face recognition algorithms is used for extraction of features and as a classifier or to recognize a face, Feed Forward Neural Network [10] is used Complete steps that how a face can be recognised and the procedure of sending messages on cell phone to the person in a category are• Mark features- purpose of this file is to mark features manually (left eye, right eye and teeth) It extracts six independent features from ICA algorithm • Align faces- This file is used to convert all the images present in jpg format into pgm format After run this step, it aligns the positions of mouth and eye of every image saves in Labels.mat 909 https://sites.google.com/site/ijcsis/ ISSN 1947-5500 International Journal of Computer Science and Information Security (IJCSIS), Vol 14, No 10, October 2016 Fig Align faces module • • Laodface.mat- Purpose of this module is reshaping and the images present in pgm format are represented in form of a matrix which is named as C Main.mat- When we run this main function, it gives result in the form of correctly matched images with the images stored in training data We are using two variables for calculating this result named as pc and rank mat Fig Pc and Rankmat output • Train- In command window of MATLAB, we write nntool, then import the data named as inputs.mat and targets.mat After loading the data, we create a new network names as network1 and select input as train_ex , output as d, network is Feed Forward Distributed Network, Training function is trainlm, and learning function is learngdm When we open this network1, we can check the performance of this network which is trained • Test- In this, after recognizing the face image and find out the category of image, messages will be send to all the persons of that category 910 https://sites.google.com/site/ijcsis/ ISSN 1947-5500 International Journal of Computer Science and Information Security (IJCSIS), Vol 14, No 10, October 2016 Using 3G Dongle, Recognition and Reporting based on Pervasive Computing Fig Testing of image This gives result of category from which image is matched Above image belongs to neighbour category If an image is recognized, we can plot its performance graph to find out the accuracy and the graph is shown below Fig Plots for Performance When the image is recognized, messages are sending to all the members of that category which is recognized The training database consists of 41 images and a sample database is shown in Fig Fig Screenshot of Training Database 911 https://sites.google.com/site/ijcsis/ ISSN 1947-5500 International Journal of Computer Science and Information Security (IJCSIS), Vol 14, No 10, October 2016 Table-1 Experimental Results S No Name of technology Recognition rate PCA + Neural Network 98% PCA+ICA +Neural Network 100% Desired results will be obtained by using 3G dongle with MATLAB in such a way that messages will receive on cell phone from the number which is registered with the dongle We can send the messages to number of people at the same time Number of commands is used to produce operations for receiving the message and this message can be received by number of people at the same time by inserting their numbers in code of MATLAB Fig Text Message on Phone IV.CONCLUSION AND FUTURE SCOPE A hybrid combination of PCA and ICA for feature extraction is proposed in this paper, Feed Forward Neural Network is used for face recognition and 3G Dongle with MATLAB is used for sending the messages on cell phone Implementation part is done by using Matlab For future work, use of sensors for an 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802.15.4 & Zigbee”, IEEE, vol 56, issue 5, pp 21-24 [17] Nojun Kwak, Chong-Ho Choi, Jin Young Choi “Feature Extraction Using ICA”, Springer, pp 568-573 913 https://sites.google.com/site/ijcsis/ ISSN 1947-5500 ... Security (IJCSIS), Vol 14, No 10, October 2016 Using 3G Dongle, Recognition and Reporting based on Pervasive Computing In first step, we take training set and pre-processing in which images are normalized... 1947-5500 International Journal of Computer Science and Information Security (IJCSIS), Vol 14, No 10, October 2016 Using 3G Dongle, Recognition and Reporting based on Pervasive Computing Fig Testing... 1947-5500 International Journal of Computer Science and Information Security (IJCSIS), Vol 14, No 10, October 2016 Using 3G Dongle, Recognition and Reporting based on Pervasive Computing [5] Manal