Lvq based person identification system
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Lvq based person identification system Lvq based person identification system Document Transcript

  • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 185 LVQ BASED PERSON IDENTIFICATION SYSTEM Nisargkumar Patel1 , Prof. V.V. Shete2 , Ashwini Charantimath3 MIT College of Engineering, Pune, India ABSTRACT A wide verity of security system requires reliable recognizable scheme to either confirm or identify the person from a group. Recently, researchers taking great interest in physiological like EEG and ECG. It is also known as EEG or ECG signature of a person. Due to characteristics of EEG signal, it is very hard to breach the system or copy the EEG signal. In this paper EEG based method is introduced for person identification. There are different methods which can be used for feature extraction. Here, in this paper Wavelet Packet Decomposition (WPD) is used. For classification Learning Vector Quantization (LVQ) is used. Keywords: EEG signal processing, Bio-metric security, person identification, Learning Vector Quantization (LVQ). INTRODUCTION The term “Biometric” is known as a system which uses human biometric characteristics for identification of a person. Body characteristics like face and voice are used to recognize each other from thousands of years [2]. Recently, researchers are focused on the biometrics system for prevent precious data or transaction from forgery. In conventional biometrics security systems are based on the biometric traits such as 1) Figure print. 2) Retinal or iris Scanning 3) DNA. 4) Voice reorganization etc [6]. Identification and authentication are two different processes, for authentication of a person it is necessary to identify the person. Identification of a person can be defines as it is the process to identify individual from a group and authentication is defines as it is the process of confirm or deny the identity claim of a person. In this method we can use any human characteristic and or physiological signal which should satisfy characteristics such as Universality, Distinctiveness, Permanence, Collectability, Acceptability, and Circumvention. The point is most of the conventional traits does not satisfy above characteristics, for example figure print and voice reorganization are not fulfil above characteristics because accidently or handicap person may not have figure print or voice so it is not universal. Solution for above problem INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), pp. 185-193 © IAEME: Journal Impact Factor (2013): 6.1302 (Calculated by GISI) IJCET © I A E M E
  • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 186 is that, we have to concentrate on the more unique characteristics of a human like ECG and EEG signals of a human. This is a unique pattern of any human in his entire life. EEG signal is the electrical signal generated by the brain during its activity and it is recorded on the scalp of the human. Here in this paper we are using an EEG pattern for the identification of a human or person, because it is different in every subject. In other words inter-subject variability is very high with EEG signal. This method for identification of a human uses EEG signal as human traits. Feature extraction of the EEG signal can b done with various methods deepens upon the application, how much accuracy is required. In this method FFT and Wavelet packet decomposition is used. Also for classification we can use both supervised and unsupervised learning methods and algorithms for artificial neural network. Here, learning vector quantization is used for the classification of signals. Se´bastien Marcel [2], was investigated the use of brain activity for person authentication. It has been shown in previous studies that the brain-wave pattern of every individual is unique and that the electroencephalogram (EEG) can be used for biometric identification. Jiang Feng Hu [16] was able to achieve accuracy ranging from 75% for subject authentication and 75% to 78.3% for identification using 6 channels. Learning Vector Quantization (LVQ) neural network was used for the classification that achieved classification scores in the range of 80% to 100%, (case dependent). METHODOLOGY OF SYSTEM The main intention of this paper is to introduce a one of the best method for human identification for improve the security systems. Since everybody have unique EEG pattern which is universal and brain damage is rarely occurred. In this system there are some steps for getting accurate result or we can say some methodology. Figure 1: Block Diagram of Proposed System Firstly, we have to acquire a signal from the subject it is the EEG signature of the subject then find out which feature suits for the application and find out the method for extraction of the same from the signal and then it will be given to the classifier which classifies it in the different class depends upon its algorithm. Selection of a classifier is also essential and it also depends upon application as well. All the steep discussed above will be explained in this section and it is illustrated in figure 1. 1. Data Acquisition Signal or data acquisition is the most important step because other steps are depending on the data. EEG data is taken from the scalp of the subject through the electrode (sensors). Superconductive gel is place in between the sensor and the scalp because signal coming from the brain through scalp is very weak. It is in terms of micro volt. Thus, better reception of the signal conductive gel is used. And signal detected from the particular node is not the signal from that EEG signal Pre- processing Security check EEG feature extraction EEG pattern classification
  • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 187 particular place where the node is placed but it is the average of the total relative potential of the scalp. Figure 2: Standard 10-20 international system for sensor placement [13] Raw EEG data is too noisy so it needs to be filtered and amplified for further processing. Subject is requiring sitting at rest with eye closed in silent room this condition makes easy to yield required rhythm. In 10-20 international system sensor is place such that distance between two sensors is either 10% or 20% of total area of the scalp. For that measurement of scalp is taken before node placement, and then it is divided in two equal spaces which contain 10% or 20% distances from each node. 2. Signal Pre-processing At the time of data acquisition signal amplification and filtration process is applied to that data. But additional digital band pass filter is applied in second stage because we need specific band of the signal EEG signal band is 0.5 to around 90 Hz. For our application we require signal of 0.5 to 64 Hz. To acquire the same band Butterworth band pass filter is applied and due to this operation some line interference and noise is also removed from the signal. EEG signal dived in four basic EEG frequency patterns which define the mental condition of a person like person is in drowsy mood or he/she is fully awake state, all patter is being described below [6]: • Delta ( ) (0.5 - 4Hz): These waves are primarily associated with deep sleep and may be with waking state. • Theta (θ) (4 - 8 Hz): These waves have been associated with access to unconsciousness creative inspiration and deep meditation it seems to be related to the level of arousal. • Alpha ( ) (8 - 13 Hz): These waves have been associated with a relaxed awareness without attention or concentration. • Beta (β) (>13 Hz): It is most evident in frontal region and associative with busy or anxious thinking and active concentration. After getting desired pattern of the EEG signal next task is to extract the features from the desired EEG pattern (8-12 Hz) with different methods. 3. Feature extraction Feature extraction is the process to separate desired output from the EEG pattern. Rather than apply the operation on EEG pattern the reason behind the feature extraction is classification process is easy with the extracted feature because, with help of feature we can easily differentiate the person. There are various methods are available for feature extraction [9] [11] [13]. Few techniques are listed below: • ARR model, • Fast Fourier Transform (FFT),
  • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 188 • Discreet Fourier Transforms (DFT), • Wavelet packet decomposition (WPC) From above techniques we can use any of it according to application. Here in this paper WPC is used for the feature extraction. 3.1.Wavelet Packet decomposition (WPD) To overcome the disadvantage of the FFT here WPD is used for feature extraction because it is able to give the solution in time as well as in frequency domain of the non-stationary signal as well. In the discreet wavelet transform, each level is calculated by passing only previous approximation coefficients through low and high pass quadrature. WPD is the wavelet transform where the signal is passed through more filter than the discrete wavelet transform (DWT) as shown in figure 3 [1]. For n levels of decomposition the WPD produces 2n different sets of coefficients (or nodes) as opposed to (3n + 1) sets for the DWT. Studies using wavelet packet decomposition to analyze EEG signals were able to obtain the four brain rhythms: alpha, beta, theta and delta. Figure 3: Wavelet Packet Decomposition over 2 levels The signal x(t) is decomposed into different scales in equation (1) [3] ‫ݔ‬ሺ‫ݐ‬ሻ ൌ ෍ ෍ ݀௝ሺ݇ሻ ߮௝,௞ሺ‫ݐ‬ሻ ൅ ෍ ܽ௄ሺ݇ሻ ‫׎‬௄,௞ሺ‫ݐ‬ሻ ∞ ௞ୀି∞ ∞ ௞ୀି∞ ௄ ௝ୀଵ . . ሺ1ሻ Where j is the scale parameter, ߮௝,௞ሺ‫ݐ‬ሻ are discrete analysis wavelets and ‫׎‬௄,௞ሺ‫ݐ‬ሻ are discrete scaling functions. ݀௝ሺ݇ሻ Are the wavelet coefficients at scale 2௝ and ܽ௄ሺ݇ሻ are the scaling coefficients at scale 2K . In this research four level of wavelet packet decomposition is use for selection of alpha band of the EEG signal which is collected from (4, 2) node of wavelet packet tree. Detail co-efficient of the signal of 8 to 12Hz are extracted for further processing. For the classification we can use different features that are listed below [1] [9] [10]: • Mean of the signal. • Standard deviation. • Entropy. • Minimum value of the signal. • Maximum value of the signal. X[n] H1 H1H2 H1L2 L1 L1H1 L1L2
  • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 189 Here in this paper detail coefficient of the alpha signal which are lies between 8 to 12 Hz are extracted. Here minimum, maximum, mean, and standard deviation these features are used for the further process. Value of each coefficient is calculated using following equations [1]: µ௫ ൌ 1 ݊ ෍ ‫ݔ‬௜ ௡ ௜ୀଵ … … … … … … … … … . ሺ2ሻ ߪ௫ ൌ ඩ 1 ݊ ෍ሺ‫ݔ‬௜ െ ߤ௫ሻଶ ௡ ௜ୀଵ … … … … … ሺ3ሻ ߝሺ‫ݔ‬ሻ ൌ െ ෍ ‫ݔ‬ଶ ሺ‫ݐ‬ሻ log ‫ݔ‬ଶ ሺ‫ݐ‬ሻ ௧ … … … … . ሺ4ሻ Thus total five feature of the one channel such four channels are used in this project so total number of feature for each data is 5*4=20 feature for each person. Lastly, all the feature of all data is store in one metrics which is used for training for the neural network. 4. Classification Many researcher use artificial neural network for the classification purposes in many areas. Classification of any signal or input is to verify or categorize in the pre-define classes. We can also say classification is the process to differentiate the input in different range. Now a day it is very popular because of its speed of calculation and data handling capacity is very high. Figure 4: Architecture of LVQ neural network In this paper learning vector quantization neural network is used learning vector quantization is a standard statistical clustering technique for classifies the input to the specified output classes after training LVQ assign weight vector to the input pattern closest to the output unit. And for training “learnlvq1” algorithm is used for the classification. Here data is given to the network for training thus, it is supervised learning technique we already know the output of the system. Basically, network output is the signal is lying in which predefined classes. Architecture of the LVQ neural network is shown below in figure 4. In this network there are basically three layer, first is input layer in that number of neuron is depends upon the number of inputs then hidden layers it can be one or more according the necessity of the application then lastly, output layer.
  • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 190 The classifier performs a series of operation with each pattern of the training set until the stopping criteria are met. When classification is done using LVQ desired class is given to the output neurons in output layer. There are no certain rules for choosing number of hidden layer neurons. Many tests are performing for optimum solution for the neural network configuration that will be explained in the experimental results. Figure 5: Flow Chart of LVQ Algorithm EXPERIMENTAL RESULTS Experiment is done with four sets of data and combination of subject is selected randomly. During the process of recording the data, subject should sit at rest with eye close in a silent room and remain calm throughout the whole process. In this experiment data is taken from channel F, C, P and O at sampling frequency 240 Hz for 3 second epoch. Here results are shown below: 1. Data of EEG signal is lying between 0.5 to about 50Hz. Here for the further process EEG signal of 0.5 to 50Hz is passing through butter-worth band pass filter which has pass-band frequency is 8Hz and stop-band frequency is 12Hz (alpha rhythm). Thus we get the alpha rhythm of subject’s signal. All three channels follow the same operation. Following figure 6 shows the filtering operation and resulting alpha rhythm of channel F3. START STOP INITIALIZE WEIGHT AND LEARNING RATE REDUCE THE LEARNING RATE CALCULATE EUCLIDEAN DISTANCE AND UPDATE WEIGHT INPUT VECTOR IF DESIRED OUTPUT? IF DESIRED OUTPUT? UPDATE WEIGHT VECTOR YES YES NO NO
  • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 191 Original signal of channel F3 Filtered signal of channel F3 Figure 6: Original signal and filter signal of channel F3 2. Four level of wavelet packet decomposition is applied in a filtered signal. Here detail coefficient of the (4, 2) component is reconstructed and used to extract the feature from every channel. Features that are used in this method are: mean minimum, maximum, standard deviation and entropy of the signal. All features are calculated with equation which is mention in above explanation. These feature are stored in a metrics which will be used for the training of a neural network, it is shows below in table 1. Table 1: feature set of Data 1 Feature set of Data 1 F3 C3 P3 O1 -2.656x103 -2.764x103 -3.283x103 -3.24x103 2.851x103 2.679x103 3.253x103 3.749x103 -3.319 -2.457 -2.734 -1.220 42.146 30.685 33.849 14.556 3.128x108 4.668x108 7.752x108 7.976x108 Learning vector quantization neural network is train by the “learnlv1” algorithm is use for the classification in this paper. The number of neuron in the input layer varied accordingly to the number of the feature is used here in this experiment 5 feature of each channel is used for the training. Output layer contains neuron according to the data base in other words it is depends on the class defined by the user. Fir each feature extraction method, the configuration that produces weight arte optimized to maximum classification, sensitivity and accuracy. Thus we can say that configuration of the classifier plays important role in the experiments to achieve best classification. Sensitivity ൌ TP ሺTP ൅ FNሻ … … … … … . ሺ5ሻ Speciϐicity ൌ TN ሺTN ൅ FPሻ … … … … … . ሺ6ሻ Accuracy ൌ TP ൅ TN ሺTP ൅ FP ൅ TN ൅ FNሻ … … ሺ7ሻ Where, TP= True positive; TN=True negative; FP=False positive; FN=False negative.
  • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 192 Figure 7: ROC of the LVQ neural network Table 2: Performance of the LVQ Neural network Parameters Data set 1 Data set 2 Data set 3 Data set 4 TP 20 20 20 20 TN 04 04 04 04 FP 00 02 03 01 FN 00 02 03 01 Sensitivity (%) 100.00 90.91 86.96 95.24 Specificity (%) 100.00 66.67 57.14 80.00 Accuracy (%) 100.00 85.71 80.00 92.31 CONCLUSION AND FUTURE WORK This experiment is implemented in Matlab 2011 and using neural network tool box. Network sensitivity, specificity and accuracy is calculated using equation using following equation and value of the each element in the table 2 and ROC of the network is also shown in figure 7. This paper proposes one of the best and unique methods for biometric security system. Role of the feature extraction from the signal is explained very well in this paper. Technique for the feature extraction is very crucial for any kind of system, because classification process is only based on that feature which is extracted from the signal. Here, Wavelet packed decomposition technique is used for it and learning vector quantization neural network performs good and give 80 to 100% accuracy for all four data set of the different subject. In future system testing for more data and maintain and improve accuracy to 100% for more number of data. Here, four channels are used for identification process; in future research can be done on how we can reduce the number of the channel for this application with same accuracy and sensitivity. It is the main part of the future work.
  • International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 193 REFERENCE [1] Howida AbdelFattah Shedeed Faculty of Computers and information Sciences Ain Shams University “New Method for Person identification in a Biometric Security System Based on Brain EEG Signal Processing” 2011 IEEE. [2] Se´bastien Marcel and Jose´ del R. Milla´n “Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation” IEEE Transaction on pattern analysis and machine intelligence vol. 29, No. 4, April 2007. [3] S. Prabhakar, S. Pankanti, and A. K. Jain, “Biometric recognition: Security and privacy concerns,” IEEE Security Privacy Magazine, vol. 1, no. 2, pp. 33–42, 2003 [4] K. Ravi and R. Palaniappan. “Leave-one-out authentication of persons using 40Hz EEG oscillations.” In The International Conference on Computer as a Tool, volume 2, Belgrade, Serbia & Montenegro, pages 1386 –1389, 2005. [5] Wu Ting, Yan Guo-zheng, Yang Bang-hua, Sun Hong “EEG feature extraction based on wavelet packet decomposition for brain computer interface”, 618–625 Elsevier Ltd 2008. [6] Anil K. Jain, Arun Ross and Salil Prabhakar “An Introduction to Biometric Recognition” IEEE Transaction on circuit and systems for video technology vol. 14, no. 1 4-20 January 2004. [7] R. Brunelli and D. Falavigna, “Person identification using multiple cues,” IEEE Trans. Pattern Anal. Machine Intelligence. vol. 12, pp. 955–966, Oct. 1995. [8] Ramaswamy Palaniappan Danilo P. Mandic “EEG Based Biometric Framework for Automatic Identity Verification” Journal of VLSI Signal Processing 49, 243–250, 2007. [9] Carlos Guerrero-Mosquera and Angel Navia Vazquez“New approach in features extraction for EEG signal detection” 31st Annual International Conference of the IEEE EMBS Minneapolis, Minnesota, USA, September 2-6, 2009. [10] Fei Su, Liwen Xia, Anni Cai, Yibing Wu , Junshui Ma “EEG-based Personal Identification: from Proof-of-Concept to A Practical System”, International Conference on Pattern Recognition 1051- 4651/10 © 2010 IEEE. [11] Gelareh Mohammadi, Parisa Shoushtari, Behnam Molaee Ardekani and Mohammad B. Shamsollahi “Person Identification by Using AR Model for EEG Signals”, Pwaset Volume 11 page 281-285 FEBRUARY 2006. [13] Abdul-bary Raouf Suleman, Toka-Hameed Fatehi “Feature Extraction techniques of EEG signal for BCI application”, 2007. [14] A. Anoklin, O. Fisher, Y. Mao, P. Vogt, E. Schalt, F. Vogel,(1999), “A genetic study of the human low-voltage electroencephalogram” Human Genetic ,vol. 90,PP. 99-112,1992. [15] P. P. Paul and M. M. Monwar, “Human iris recognition for biometric identification” Proceedings of 10th International Conference on Computer and Information Technology, 2007, pp. 44-48. [16] H. Jian-Feng," Multi-feature biometric system based on EEG signals", In Proceedings of the 2nd International Conference on Interaction Sciences, Seoul, Korea, 2009, pages 1341–1345. [17] S. N. Sivanandam , S sumathi and S. N deepa “Introduction to neural networks using MATLAB 6.0”, The Mcgraw-Hill 2010. [18] Donna L. Hudson and Maurice E. Cohen “Neural network and artificial intelligence for biomedical engineering”, IEEE press 2000. [19] Imteyaz Ahmad, F Ansari and U.K. Dey, “A Review of EEG Recording Techniques”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 3, Issue 3, 2012, pp. 177 - 186, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. [20] Sayeesh and Dr. Nagaratna P. Hegde, “A Comparison of Multiple Wavelet Algorithms for Iris Recognition”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013, pp. 386 - 395, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [21] V.V.Ramalingam, S.Ganesh kumar and V. Sugumaran, “Analysis of EEG Signals using Data Mining Approach”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 206 - 212, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.