Neural Network Based Authentication using Compound Gait Biometric Features

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1 Neural Network Based Authentication using Compound Gait Biometric Features C. Nandini 1, Mohammed Tajuddin 2 1 Dept. of CSE, DSATM, Bangalore 2 Dept. of CSE, DSCE, Bangalore Abstract : Authentication of user based on the human gait features, Security is a major aspect in all the fields. In response to this demand of biometric system to secure the information over the internet using human gait features. Gait biometric has some unique features compare to other biometrics system. In this paper we cover the authentication of human using the wavelet decomposition and geometrical features of gait biometric system. Keywords: Biometric, Gait recognition, geometric. I. INTRODUCTION In the present scenario security is a major challenge in all the fields, in response to this demand of biometric system increasing to secure the computer system using the human biometric information such as finger print, face, retina, hand geometry and iris etc. Since all the biometric system provides reliable and efficient information to authenticate the users. Authentication methods are classified as Knowledge based authentication Possession based authentication Biometric based authentication Gait recognition which concerns recognizing individuals by the way they walk, not the way they look. Biological or behavioral characteristics as fingerprint, face or iris generally require physical contact views from certain aspects a cooperative subject. These methods cannot reliably recognize non cooperating individuals at a distance in the real world under changing environmental conditions. Gait is a relatively new biometric without these disadvantages. Gait recognition is an attractive method of identification due to the non-contact, non-intrusive nature of data acquisition and possibility of recognition at distance. However, gait also has some limitations, it can be affected by clothing, shoes or environmental context. Moreover, special physical conditions such as injury can also change a person s walking style. It reduces the discriminating power of gait as a biometric but the inherent gait characteristic of an individual still makes it irreplaceable and useful in visual surveillance. II. RELATED WORK Gait recognition system classified depending on the sensor used into three groups. FS based, WS based, MV based. In Floor Sensor (FS) based approach, a set of sensors or force plates are installed on the floor such sensors enable to measure gait related features, when walks on them. Middleton et al. [2] Used three features, stride length, stride cadence and time on toe to time on heel ratio for recognition. All Rights Reserved 237

2 recognition rate based on data set from 15 individuals. In Wearable sensor (WS) based gait recognition, gait is collected using body worn motion recording (MR) sensors. The MR sensors can be worn at different locations on the human body. MR sensor was carried in the trousers pocket % recognition rate was achieved. Most of the current gait recognition methods are MV (Motion Vision) based. In this category, gait is captured using a video-camera from distance. Video and image processing techniques are employed to extract gait features for recognition purposes. Mainly divided into two groups, appearance based methods and model based methods. Appearance based method can be subdivide into two types, state space methods and spatio-temporal methods. Most of the MV-based gait recognition algorithms are based on the human silhouette. III. RESEARCH WORK Video is the technology of electronically capturing, recording, processing, storing, transmitting, and reconstructing a sequence of still images resenting scenes in motion. These sequences of images are extracted from the video. Feature extraction with respect to geometrical features of the individual such as height, width and area Performing wavelet decomposition for the above extracted features Classification and recognition of the feature set based on the artificial neural network. Biometrics recognition system involves the preprocessing task. In biometric gait recognition system the database to be collected is in video form, so the frame of a walking person to be created. These frames are converted into silhouette. Major preprocessing tasks in gait recognition system are silhouette extraction and features extraction of a frame. Silhouette from frame can be extracted using the image processing operations. These processes are described as shown below. 3.1 Silhouettes Extraction The motional of a individual silhouette must be detected before getting the gait feature. Back ground subtraction is the relatively simple and new approach to find silhouette from image as shown in Figure.1 Figure. 1 Example of gait detection. (a) Background image, (b) Original image and (c) Extracted silhouette. In our experiment the camera is assumed to be static and that the body in the field of view is not occluded from each frame. The whole process of silhouette extraction is described as follows: To obtain an approximate background from the image sequence of a walking people, first mean image is computed by averaging the gray-level at each pixel over the entire image sequence (in Figure.1). Let Ik (x, y), k=1,2,. N, represent sequence of N images. Back ground images b(x, y) can be computed as shown in eq. All Rights Reserved 238

3 b (x, y) = median (Ik (x,y)), where k =1, 2, 3.. N...(1) Moving object is extracted by back ground subtraction. Image processing operation likes Erosion, dilation are applied to improve the quality of extracted silhouette, and reduce noise. 3.2 Feature Extraction We refer an individual pixel is located at the row i and column j by the notation B(i, j) = the brightness of the image at the point (i, j ).(2) At the time of walking, the human body is center of mass change from instance to instance so we are using center of mass as a feature this center of mass show the bright weighted average of x and y coordinates pixels in the frame. Center of mass of the white pixels area for binary images is the same as the center of mass if we consider the intensity at a point as the mass of that point. In the binary image we can calculate center of a mass coordinate by using following eq. 3, eq.4 and eq.5. (3).. (4) Here, and are center of a mass points in image. Here m and n are the dimension of matrix which store image in the matrix form, A is the area of region, it can be calculated by following formula... (5) Another feature of gait is its periodicity [10]. By observing, the width of the silhouette was changing periodically with the time lapse. The width of the silhouette will reach a maximum when the two legs are farthest apart (full stride stance) and drop to a minimum when the legs overlap (heels together stance). At the same time, the height of a silhouette has slight change in the procedure. Consequently, we can get the estimation of gait cycle. For calculating the step size length and cycle length, we are using boundary box technique. Figure 2. Area = Height * width In silhouette boundary box is created, so that cover the whole object from outside and its right edge boundary touches the back foot back end and left edge touches the front foot front end, this boundary box width recorded as step size All Rights Reserved 239

4 Gait cycle is one way to specify people (as shown in Figure 2). For particular person it begins when one foot contacts the ground and ends when that foot contacts the ground again. Thus, each cycle begins at initial contact with a stance phase and proceeds through a swing phase until the cycle ends with the limb's to next initial contact. Stance phase accounts for an approximately 60 percent, and swing phase for approximately 40 percent, of a single gait cycle. For calculating cycle length we are created stack of frames. Figure 3 shows the complete gait cycle. Figure 3: Gait cycle 3.3 Shoulder features from gait At the time of walking, the human body shoulders movement changes from instance to instance so we are using the shoulder of x and y coordinates pixels in the frame. In this approach we are selecting the region of interest of a shoulder gait frame. By using the SURF and Harris approach to detect he corner points of an image of the selected region as shown in Figure 4. SURF a single-valued function defined over a geometrically rectangular grid, and uses resultant matrix, assumed to be the same size as Z, to color the surface. Fig.4 Input image & selected region The input gait image as shown in figure 4 and the region of interest to find the shoulder coordinates X and Y pixels as shown in table 1. Table 1 X & Y coordinates values of two shoulders. X Y All Rights Reserved 240

5 38 65 Figure 5. Coordinate values of gait frame 1 and frame 2. Figure 5 shows the change of pixel values between the gait frame 1 and frame 2. The gait shoulder features is unique for everyone in gait patterns. Since this features can also be used to authenticate the user. 3.3 Neural Network A neural network can be seen as machine that is designed to model the way in which the brain perform a particular task or function of interest. By offering useful properties and capabilities, i. e. nonlinearity, input output mapping, neural network can be applied in many field. Neural networks have been used among others to identify system as well as data classification techniques with significant success rate. Neural network composes of input layer, hidden layer and output layer Input Layer This layer takes the inputs (the values we pass) and forwards it to hidden layer. Input layer for created neural network is determined by characteristics of inputs. We have four attribute feature vector. So number of neuron in input layer is four Hidden layer Hidden layer automatically extracts the feature of the input pattern. There is no definite rule to find the number of neuron in hidden layer. So, it is hit and trial method. Where network to be tested with different neuron in hidden layer. We were found that 40 neuron hidden layer could accomplish the task with good recognition rate. For hidden layer hyperbolic tangent sigmoid transfer function is used. At the hidden layer it is used to calculate network output from its input. The tangent hyperbolic function and its fast approximation is given by following eq.6..(6) Where is i th element of a1 vector contains output from the hidden neurons. is the i th element of n1 vector containing net input going into the hidden units. n1 is calculated by using the following eq.7. (7) Where p is input pattern b1 is the vector of bias. is the weight matrix between hidden layer and output layer Output layer Output layer is designed based on the required output of neural network. Pure linear activation function is selected for output, given by following eq.8... All Rights Reserved 241

6 Where is the column vector coming from output layer. is the output net inputs going into the output layer can be calculated by using following eq.9. (9) Where, b2 is the bias at second layer, w21 is the synaptic weights at hidden layer and output layer, a1 is the column vector. Figure X shows the network structure with one input layer, one hidden layer, and one output layer. It is network structure. x=[x1, x2, x3, x4] is the training sample and network input. Y = [y1, y2, y3,.., y25] is the output vector. Two important parameters are learning rate and weight range that can influence the performance of neural networks. Figure 6 shows the network structure with one input layer, one hidden layer, and one output layer. It is network structure. x=[x1, x2, x3, x4] is the training sample and network input. y= [y1, y2, y3 y25] is the output vector. Two important parameters are learning rate and weight range that can influence the performance of neural networks. 4. Experimental Results The proposed method is tested on CASIA Dataset B which contains walking style of 20 persons. Each person as 12 image sequences of three directions i.e. parallel, 45 degrees and 90 degree to the image plane. The length of each sequence is not identical for the variation of the walker s speed and it ranges from 37 to 127. The plot of height and width data that has been collected from a walking subject at two different time instance and the plot of height and width data that has been collected for two different subjects with to recognition is shown in Figure 7. The comparison of various approaches to authenticate the user based on biometric features is shown in the Table3. In our approach we used maximum features of gait such as height, width, area and shoulder features and finally KNN approach for classification techniques and the results are shown in Table 3 Table 2: The results at two different time instances. Frame Height Width Area Mean All Rights Reserved 242

7 Table 3. Comparison with approaches No Approach Accuracy Proposed approach DWT and shoulder features. 92% Wavelet transform approach feature extraction and KNN classifier [10]. 91% Gait Authentication Based on Fusion of Wavelet and Hough Transform[4] 90% Hough transform to obtain the joint angles from the body segment and morphological 72% operation [7] IV. CONCLUSION A gait recognition is implemented which combines wavelet coefficients along with the four geometrical features namely height, width, area and shoulder area of a subject. The proposed method is tested on CASIA Dataset B and an improved recognition rate is achieved. We have used the ANN method based on Levenberg- Marquard method for the purpose of training the dataset. Experimental results prove that our system has higher recognition rate. In general, the recognition rates were found to be good when decision fusion is taken by combining haar wavelet coefficients and geometrical feature. Figure 7: Obtaining the approximation coefficients of All Rights Reserved 243

8 References Lee, T.K., M. Belkhatir, S. Sanei, A comprehensive review of past and present vision-based techniques for gait recognition, Multimedia Tools Appl., 72(3): Sivarathinabala, M. and S. Abirami, Automatic Identification of Person using fusion of features, in proceedings of ICSEMR pp: 1-5, IEEE,DOI: / ICSEMR Jyothi Bharti, M.K. Gupta, "Gait Recognition using shoulder body joints", IJCA, Vol 53, No 4, C.Nandini and Sindhu K," Gait Authentication Based on Fusion of Wavelet and Hough Transform ", IJET,Vol 2, No3 March C. Nandini, Pratul Mukhopadhyay, Tushar Tanmay, Saurabh Kumar Ranjan, Samvit Roy, An Efficient Human Identification Using Gait Analysis, IJRRCE, Vol. 1, No. 2, June 2011 Yi-Bo Li and Qin Yang, Gait extraction and recognition based on lower leg and ankle, International Conference on Intelligent Computation Technology and Automation, Hu Ng, Wooi-Haw Tan, Hau-Lee Tong, Junaidi Abdullah, Ryoichi Komiya, Extraction of Human Gait Features from Enhanced Human Silhouette Images, International Conference on Signal and Image Processing Applications, IEEE, Hao Zhang and Zhijing Liu, Gait Representation and Recognition Using HaarWavelet and Radon Transform, ICI Engineering, IEEE, Yumi Iwashita, Ryo kurazume, Person Identification from Human Walking Sequences using Affine Moment Invariants, International Conference on Robotics and Automation, IEEE, Saeid Rahati, Reihaneh Moravejian, Farhad Mohamad Kazemi, Gait Recognition Using Wavelet Transform, Fifth ICIT, New Generations, IEEE, Garcia, M., Chatterjee, A., Ruina, A., Coleman, M.: The simplest walking model: stability, complexity, and scaling. ASME Journal of Biomechanical Engineering 120 (1998) Davis, J.W., Bobick, A.F.: The representation and recognition of human movement using temporal templates. In: IEEE Computer Vision and Pattern Recognition. (1997) Laszlo, J., van de Panne, M., Fiume, E.: Limit cycle control and its application to the animation of balancing and walking. In: SIGGRAPH 96. (1996) Murray, M.P., Bernard, A., Kory, R.C.: Walking patterns of normal men. The Journal of Bone and Joint Surgery 46A (1964) Imed Bouchrika & Mark S. Nixon, Gait Recognition by Dynamic Cues, University of All Rights Reserved 244