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CHAPTER 1INTRODUCTION There are several reasons for recent increased interest in face recognition

CHAPTER 1INTRODUCTION There are several reasons for recent increased interest in face recognition, including rising public concern for security, the need for face analysis are modeling techniques in multimedia data management and computers entertained. Face recognition being the most important biometric, trail it still faces many challenges, like illumination. Identifying faces through a digital eye is not an easy nut to crack. Whenever face recognition is used across the surveillance system it is often very difficult to acquire the faces in controlled environment. So there has to be a system which is capable of recognizing the face capture even in poor lighting conditions. Although many approaches have been proposed during last decade however, real world scenarios remain a challenge moreover, all the techniques are greatly affected by variations and their performance get degraded when variations and in illumination. The illumination problem arise when the same face appears differently due to the change in lighting and pose variations comes from the fact when there exits head rotation. CHAPTER 2LITERATURE SURVEY 1. Eigenfaces for recognition, by Mathew Turk and Alex Pentland, Vision and modeling group, The media Laboratory, Massachusetts Institute of Technology. In this paper author mainly focuses on developing computational model of face recognition. They suggest the scheme based on an information theory approach that decomposes face images into a small set of characteristic feature image called eigenfaces. 2. Face Detection based on Skin Color in Image by Neural Networks, Aamer .S.S.Mohamed, Ying Weng, Stan S Ipson, Jianmin Jiang School of Informatics, University of Bradford . This paper proposes a new algorithm for face detection. They exctracted DCT coffecient vector features after segmentation a face skin candidate using skin color information. 3. Jeffery, R. Price Timothy F. Gee, suggests under the title of Towards Robust Face Recognition from video, A novel, template-based method for face recognition. Three confounding factors expression, illumination, and decoration are considered in this paper. The goal of their work is to develop a recognition engine to serve as the foundation for video surveillance system. 4. Hongliang Li, King N. Ngan, and Quang Liu proposed in their paper FaceSeg Automatic Face Segmentation for Real-Time Video, an accurate unsupervised Human face segmentation method, which extracts faces from real-time video. They have presented a new matting algorithm to estimate the alpha-matte based on an adaptive trimap generation method. 5. Sutjipto Arifin and Peter Y. K. Cheung presented in the paper A Novel Video Parsing Algorithm utilizing the Pleasure-Arousal-Dominance Emotional information, a novel episode segmentation algorithm that utilizes the emotional content of the video. 6. Jiangwei Li and Yunhong Wang. Suggests two approaches in his paper Face Indexing and searching from videos. One approach is to index personal faces in a video and second approach is search similar samples with face image as a query. This is helpful in many ways e.g. making personal albums, searching a specific person in a video. 7. Tarik Arici and Salih Dikbas proposed SALSA (Skin-Aware Local ContraSt enhancement) algorithm in their paper Skin Aware Local Contrast Enhancement. 8. V. Patanavijit and S. Jitapunkul presented a video enhancement method using SRR framework for real standard sequences that are corrupted by any noise models in their paper Video enhancement using an iterative multiframe SRR based on a Robust Stochastic Estimation with an improved observation model. 9. Integrating Face and Gait for Human Recognition at a Distance in Video, by Xioli Zhou and Bir Bhanu, Fellow, IEEE. This paper introduces a video-based recognition method to recognize noncooperating individuals at a distance in video expose side views to the camera. The features of face and gait are obtained. 10. A Video Database of Moving Faces and People. By Alice J. OToole, Joshua Harms, Sarah L. Snow, Dawn R. Hurst, Mattew R. Pappas, Janet H. Ayyad and Harve Abdi. In this paper author describe a database of image and video clips of faces and people. This database is useful for testing algorithms for face and person recognition. 11. Improving Face Recognition Rate by Combining Eigenface Approach and Case-based Reasoning by Haris Supic. In this paper author combines eigenface approach and case-based reasoning. 12. Color Face Recognition for degraded Face Images. By Jae young Choi, Yong Man Ro, and Konstantinos N. Plataniotis. In this paper author proposes new metric called variation ratio gain (VRG) to prove significance of color effect on low-resolution faces. 13. Comparing Robustness of Two-Dimensional PCA and Eigenfaces for Face Recognition. By Muriel Visani, Christophe Garcia, and Christophe Laurent. This paper is evaluating the robustness of 2D PCA for face recognition and comparing it with eigenfaces method. In this work they have tested image rotation, scaling, vertical and horizontal translations, Gaussian blurring, addition of white noice and partial occlusions of the face. 14. Video-based framework for face recognition in video by Dmitry O. Gorodnichy, IIT-ITI, NRC-CNRC, Canada K1A 0R6. In this paper author concentrated on problems of face recognition in video (FRiV). In this paper differentiation is made between photographic facial data and video-acquired facial data. Use of neuro-associative principle employed by human brain, according to which both memorization and recognition of data are done based on a flow of frames rather than on one frame. 15. Face detection and Recognition in an image sequence using Eigen ediginess. By B. S.Venkatesh, S. Palanivel and B. Yegnanarayana, Dept. of Computer science and Engineering. IIT, Madras, Chennai. This paper describes a system for face detection and recognition in an image sequence. Motion information is used to find the moving regions. Eigen analysis of edginess representation of face is used for face recognition. 16. Fast Face Detection Using QuadTree Based Color Analysis and Support Vector Verification by Shu-Fai Wong and Kwan-Yee Kenneth Wong, Dept. of computer science and Information systems, The University of Hong Kong. This paper proposes a multi-scale face detection scheme using QuadTree to reduce time complexity. The propsed scheme uses Skin color as a heuristic feature, and detect face. Skin color can be identified by comparing the given pixel with the skin color distribution or model. 17. Video face recognition a Physiological and behavioural multimodal approach. By Federico Matta, Jean-Luc Dugelay. Eurecom Institute 2229 Route des Cretes, 06904 Sophia Antipolis, France. In this paper author present a multimodal person recognition system, composed of two modules one is based on behavioral information and second one is probabilistic extension of the classic Eigenface approach. 18. Probabilistic recognition of human faces from video , by Shaohua Zhou, Volker Krueger, and Rama Chellapa. This paper propose a tracking-and-recognition approach which attempts to resolve uncertainties in tracking and recognition simultaneously in a unified probabilistic framework. CHAPTER3 AIM, OBJECTIVE AND METHODOLOGY. 3.1 AIM The goal of this system is to detect and locate human faces. The face recognition system is robust enough to detect faces in different lightning conditions and to recognize the face with less misclassification. 3.2 OBJECTIVES To be given a video. Extract an image from image sequence of the video. Compare the images, good or bad. Detect the face. Overcome the difficulties against variation in illumination. 3.3 METHODOLOGY Image processing- Images are cropped such that they avoid facial image remains, and color images are normally converted to black and white in order to facilitate initial comparisons based on grayscale characteristics. First the presence of faces or face in a scene must be detected. Once the face is detected, it must be localized and normalized process may be required to bring the dimensions of the live facial sample in alignment with the one on the template. All facial scan systems attempt to nature visible facial features in a fashion similar to the way people recognize one another. The features most often utilized in facial scan systems are those least likely to change significantly over time upper ridges of the eye sockets, areas around the cheekbones, sides of the mouth, nose shape, and the position of the major features relative to each other. Behavioral changes such as alteration of hairstyle, changes in makeup, growing or showing facial hair, adding or removing eyelashes are behaviors that impact the ability of facial scan system to locate distinctive features, facial scan systems are developing to the point where they can overcome such variables. Template matching- It compares match templates against enrollment templates. A series of images is acquired and scored against the enrollment, so that a user attempts take place within 1 to 2 seconds. Facial scan is not as effective as finger-scan or iris-scan in identifying a single individual from a large database, a number of potential matches are generally return after large-scale facial-scan identification searches. Facial recognition software is based on the ability to first recognize faces, which is technological feat in itself. It you look at the mirror, you see that your face has certain distinguishable land marks. These are the peaks and valleys that make up the different facial features. There are 80 nodal points that are measured by the software Distance between the eyes Width of the nose Depth of the eye socket Cheekbones Jaw line Chin Challenges in the recognition system – Though face recognition have been a grown up research area, however, there still remain many problems that must be overcome to develop a robust face recognition system that works well under various circumstances. Illumination Pose Occlusion Expression Illumination and expressions Illumination and pose But under uncontrolled environments their performance gets deteriorated mainly due to variations in illumination and head rotations. Such variations have proven to be one of the biggest problems of face recognition system. CHAPTER 4 DESIGN OF SYSTEM 4.1.1 HARDWARE SOFTWARE REQUIREMENT- Hardware Intel core 2 duo 2 GB RAM, 320 GB HDD, DVD RW, Multimedia Keyboard, Optical mouse, monitor, Web camera. Software MATLAB, Window OS. CHAPTER 5 SOFTWARE DESIGN 5.1 SOFTWARE DESIGN STEPS 1. Take video sequence. 2. Separate the frames from that sequence. 3. Select the frames having faces. 4. Choose the frames having best faces. 5. Enhance the images. 6. Frames may have one or more faces in it. 7.Apply viola-jones algorithm for face detection. 5.1.1Face Extraction Acquiring pictures to computer from camera and process medium (environment) via frame grabber is that the first step in face recognition system applications. The input image, within the form of digital data, is distributed to Face Extraction formula a part of a software package for extracting every face within the image. A lot of analysis has been done in this area, most of thats efficient and effective for still pictures only therefore couldnt be applied to video sequences directly. Within the video scenes, human faces will have unlimited orientations and positions, therefore its detection is of a variety of challenges to researchers. Generally, there are three main processes for face detection supported video. At first, it begins with frame based mostly detection. During this process, many traditional strategies for still pictures will be introduced like statistical modeli ng method , neural network-based method , SVM-based method , HMM method , BOOST technique and color-based face detection , etc. However, ignoring the temporal info provided by the video sequence is that the main disadvantage of this approach. Secondly, integrating detection and tracking, this says that detective work face within the initial frame so chase it through the full sequence. Since detection and chase square measure freelance and knowledge from one supply is simply in use at only once, loss of knowledge is inevitable. Finally, rather than detective work every frame, temporal approach exploits temporal relationships between the frames to detect multiple human faces during a video sequence. In general, such technique consists of two phases, namely detection and prediction so update-tracking. This helps to stabilize detection and to create it less sensitive to thresholds compared to the other two detection categories. Steps involved in face extraction Figure SEQ Figure ARABIC 1 Face Extraction steps 5.1.2 Face Detection Output of face extraction stage, is input for face detection stage. Detection can be location of face region as a whole, and location of face region with facial features (i.e. eyes, mouth, eyebrow, nose etc.). Detection methods in the literature are difficult to classify strictly, because most of the algorithms are combination of methods for detecting faces to increase the accuracy. The basic principle of Viola-Jones algorithm is to scan a sub-window capable of detecting faces across a given input image. The standard image processing approach would be to rescale the input image to different sizes and then run the fixed size detector through these images. This approach turns out to be time consuming due to the calculation of the different size images. On the other side Viola-Jones rescale the detector instead of the input image and run the detector many times through the image each time with a different size. Viola-Jones has devised a scale invariant detector that requires the same number of calculations whatever the size. This detector is constructed using integral image and some rectangular features reminiscent of Haar wavelets. Viola-Jones algorithm consist of four stages Haar features selection, Creating an integral image, Adaboost Training, Cascading Classifiers. All human faces share some similar properties. These regularities may be matched using Haar features. Few properties common to human faces – the eye region is darker than the upper-cheeks the nose bridge region is brighter than the eyes. Instead of finding the faces, the algorithm should discard non-faces. The idea behind this statement is that it is faster to discard non-faces than to find a face. With this idea, a detector consisting of only one (strong) classifier suddenly seems inefficient since evaluation time is constant. Hence there is need of cascaded classifier. The cascaded classifier consists of stages each containing strong classifier. The job of each stage is to determine whether a given sub-window is definitely not a face or maybe a face. When a sub-window is classified to be a non-face by a given stage it is discarded. If sub-window classified as a maybe-face it is passed on to the next stage in the cascade. It means that the more stages a given sub-window passes, the higher the chance the sub-window actually contains face. 5.1.3Face Recognition Detection application is used to find position of faces in a given frame and recognition stage is used to classify images with known structural properties. 5.1.3 ALGORITHM 1.The Viola Jones Algorithm The simple principle of the Viola-Jones set of rules is to scan a sub-window capable of detecting faces across a given input photo. The trendy photo processing method might be to rescale the input photo to one of a kind sizes after which run the fixed length detector through these pix. This method turns out to be instead time consumption because of the calculation of the exclusive size images. Contrary to the standard approach Viola-Jones rescale the detector instead of the enter photo and run the detector frequently via the picture each time with a extraordinary size. At first one may suspect both strategies to be similarly time consuming, however Viola-Jones have devised a scale invariant detector that calls for the identical range of calculations whatever the length. This detector is constructed the usage of a so-known as indispensable photo and a few simple rectangular features harking back to Haar wavelets. The subsequent section elaborates in this detector. Step 1 The scale invariant detector The first step of the Viola-Jones face detection set of rules is to turn the enter image into an vital photo. This is performed with the aid of making every pixel identical to the complete sum of all pixels above and to the left of the worried pixel. This is demonstrated in Figure 2. Figure SEQ Figure ARABIC 2 The Integral Image This allows for the calculation of the sum of all pixels inside any given rectangle using simplest four values. These values are the pixels inside the critical image that coincide with the corners of the rectangle in the input photograph. This is verified in Figure below. It has now been established how the sum of pixels within rectangles of arbitrary length can be calculated in steady time. The Viola-Jones face detector analyzes a given sub-window using functions such as two or more rectangles. The exclusive varieties of functions are shown in Figure below. Each function results in a single charge which is calculated by subtracting the sum of the white rectangle from the sum of the black rectangle. Viola-Jones has empirically determined that a detector with a base decision of 2424 pixels offers first-rate consequences. When taking into account all viable sizes and positions of the capabilities in Figure 4 a overall of approximately a 16000 exclusive capabilities can then be built. Thus, the amount of feasible capabilities massively outnumbers the 576 pixels contained within the detector at base resolution. These functions may seem overly easy to carry out such a complicated undertaking as face detection, but what the functions lack in complexity they maximum truely have in computational efficiency. One may want to understand the capabilities because the laptops way of perceiving an input photo. The hope being that a few features will yield large values whilst on top of a face. Of route operations should additionally be accomplished at once at the raw pixels, however the variant due to special pose and individual traits could be anticipated to impede this technique. The purpose is now to smartly construct a mesh of functions able to detecting faces and that is the subject of the subsequent phase. Step 2. The modified AdaBoost algorithm As said above there can be calculated approximately a 160000 characteristic values inside a detector at base decision. Among a majority of these functions some few are anticipated to provide almost continually excessive values while on pinnacle of a face. In order to discover these features Viola-Jones use a modified version of the AdaBoost set of rules evolved by using Freund and Schapire in 1996. AdaBoost is a gadget learning boosting algorithm capable of building a robust classifier through a weighted aggregate of vulnerable classifiers. (A weak classifier classifies efficaciously in most effective a little bit greater than half the cases.) To suit this terminology to the offered idea each function is considered to be a ability vulnerable classifier. A vulnerable classifier is mathematically described as Where x is a 2424 pixel sub-window, f is the applied function, p the polarity and the threshold that makes a decision whether or not x have to be labeled as a wonderful (a face) or a terrible (a non-face). Since simplest a small quantity of the viable one 160000 characteristic values are expected to be potential susceptible classifiers the AdaBoost set of rules is modified to select handiest the pleasant features. Viola-Jones changed AdaBoost algorithm is offered in pseudo code in Figure below An critical a part of the changed AdaBoost set of rules is the dedication of the satisfactory characteristic, polarity and threshold. There seems to be no smart solution to this hassle and Viola-Jones advocate a simple brute force approach. This method that the dedication of each new vulnerable classifier involves comparing each function on all of the training examples for you to discover the satisfactory acting feature. This is predicted to be the maximum time consumption a part of the training method . The great acting feature is selected based totally at the weighted errors it produces. This weighted mistakes is a function of the weights belonging to the education examples.The weight of a efficiently categorised instance is decreased and the load of a misclassified example is saved consistent. As a result its far more costly for the second one function to misclassify an instance also misclassified via the primary characteristic, than an example classified effectively. An opportunity interpretation is that the second feature is compelled to awareness tougher at the examples misclassified by means of the primary. The point being that the weights are a crucial a part of the mechanics of the AdaBoost set of rules. With the vital photo, the computationally efficient functions and the changed AdaBoost algorithm in location it looks as if the face detector is ready for implementation, but Viola-Jones have one extra ace up the sleeve. Step 3 The cascaded classifier The basic precept of the Viola-Jones face detection algorithm is to experiment the detector in many instances thru the equal photograph on every occasion with a new size. Even if an photograph need to contain one or extra faces its miles apparent that an immoderate massive quantity of the evaluated sub-windows would nevertheless be negatives (non-faces). This awareness leads to a one-of-a-kind system of the hassle Instead of finding faces, the set of rules must discard non-faces. The notion in the back of this declaration is that it is quicker to discard a non-face than to discover a face. With this in thoughts a detector which include only one classifier all at once seems inefficient for the reason that assessment time is regular irrespective of the input. Hence the want for a cascaded classifier arises. The cascaded classifier is composed of stages each containing a robust classifier. The task of each degree is to determine whether a given sub-window is without a doubt no longer a face or perhaps a face. When a sub-window is assessed to be a non-face by a given level its miles immediately discarded. Conversely a sub-window labeled as a perhaps-face is surpassed directly to the subsequent degree within the cascade. It follows that the extra degrees a given sub-window passes, the higher the chance the sub-window genuinely incorporates a face. The idea is illustrated with two stages in Figure below In a single stage classifier one would usually be given false negatives as a way to lessen the fake nice fee. However, for the first ranges within the staged classifier fake positives are not taken into consideration to be a hassle for the reason that succeeding levels are predicted to sort them out. Therefore Viola-Jones prescribe the attractiveness of many fake positives in the initial stages. Consequently the quantity of false negatives in the final staged classifier is predicted to be very small. Viola-Jones additionally discuss with the cascaded classifier as an attentional cascade. This call means that more interest (computing energy) is directed closer to the areas of the photo suspected to comprise faces. It follows that when training a given level, say n, the poor examples should of course be False negatives generated by way of level n-1. IMAGE ENHANCEMENT The principal objective of image enhancement is to process a given image so that the result is more suitable than the original image for a specific application. It accentuates or sharpens image features such as edges, boundaries, or contrast to make a graphic display more helpful for display and analysis. The enhancement doesnt increase the inherent information content of the data, but it increases the dynamic range of the chosen features so that they can be detected easily The greatest difficulty in image enhancement is quantifying the criterion for enhancement and, therefore, a large number of image enhancement techniques are empirical and require interactive procedures to obtain satisfactory results. Image enhancement methods can be based on either spatial or frequency domain techniques. CHAPTER 6-APPLICATIONS 6.1DOOR UNLOCKING USING FACE RECOGNITION- 6.1.1HARDARE REQUIREMENTS- 1.Arduino Uno R3 2.Servo Motor 3.Buzzer 4.Adapter 5.M2F connectors 6.USB to serial convertors ARDUINO The Arduino Uno is a microcontroller board based on the ATmega328 . It has 14 digital input/output pins (of which 6 can be used as PWM outputs), 6 analog inputs, a 16 MHz crystal oscillator, a USB connection, a power jack, an ICSP header, and a reset button. It contains everything needed to support the microcontroller simply connect it to a computer with a USB cable or power it with a AC-to-DC adapter or battery to get started SERVO MOTOR USB TO SERIAL CONVERTOR A USB adapter is type of convertor which is used for converting USB data to and from other communication standards. It is used to convert USB data to serial port data and vice-versa. Arduino to serial micro get 5v Tx and Rx lines from a computer USB port. 1.Photography Edit 2.Marketing Edit 3. Payments 4. Access and security 5. Criminal identification 6. Advertising 7. Healthcare 6.1.2 SYSTEM DESIGN How about if the door unlocks automatically without the owner of the house moving from his position. The face the visitor is enough to let know whether he is a know person finally in the ideal state the buzzer beeps to know that the visitor is unknown The first step is to detect the face in the video once we get the region of interest containing the face, the face is captured. The next step is to recognize the face of the authorized person and the door opens. If the visitor is unauthorized the buzzer plays its role. An input is serially taken from MATLAB through the USB to serial convertor The image of authorized person is given to the system Arduino will compare the input image with the image of the visitor. If its 1 it will consider it valid and open the door. If its 2 then we will close the door and buzzer will be on for a second. CHAPTER 7 TEST AND RESULTS CHAPTER 8CONCLUSIONS In this project we have proposed a method for video based recognition that relies on good bad frames for integrating illumination for describing the appearance of a video sequence. We started with the brief exposition of the theoretical result followed by methods for learning the system. Then we described our recognition algorithm that relies on synthesis of video sequences under the conditions of illumination. Finally, we demonstrated the effectiveness of the method on video database with large and arbitrary variations in pose illumination. The efficiency of the proposed system is based priority on illumination variation. This method is verified the limitations of the scheme are observed through testing and debugging codes and then, limited by MATLAB performance the speed of the face recognition system is evaluated. CHAPTER 9REFRENCES 1 Mathew Turk and Alex Pentland. Eigenfaces for recognition, Journal of cognitive Neuroscience Volume 3 (1), 1991. 2 Jeffery R. Price, Timothy F. Gee. Towards Robust Face Recognition from video. Image Science and Machine Vision Group, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6010. 3 Le Ha Xuan, Supot Nitsuwat. Face Recognition in video, a combination of engenface and adaptive skin-color model, International Conference on Intelligent and Advance Systems, 2007. 4 Hongliang Li, King N. Ngan, Quang Liu. FaceSeg Automatic Face Segmentation for Real-Time Video, IEEE Transaction on Multimedia, Vol. 11, No. 1, January 2009. 5 Sutjipto Arifin and Peter Y. K. Cheung. A Novel Video Parsing Algorithm utilizing the Pleasure-Arousal-Dominance Emotional information, IEEE, ICIP, 2007. 6 Jiangwei Li and Yunhong Wang. Face Indexing and searching from videos, IEEE, ICIP, 2008. 7 Tarik Arici and Salih Dikbas. Skin Aware Local Contrast Enhancement. IEEE, ICIP, 2007. 8 V. Patanavijit and S. Jitapunkul. Video enhancement using an iterative multiframe SRR based on a Robust Stochastic Estimation with an improved observation model., IEEE, ICASSP, 2008. 9 Xioli Zhou and Bir Bhanu. Integrating Face and Gait for Human Recognition at a Distance in Video, IEEE Transactions on Systems, Man, and Cybernetics- Part-B Cybernetics, Vol. 37, No. 5, October, 2007. 10 Alice J. OToole, Joshua Harms, Sarah L. Snow, Dawn R. Hurst, Mattew R. Pappas, Janet H. Ayyad and Harve Abdi. A Video Database of Moving Faces and People. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 27, No. 5, May, 2005. 11 Haris Supic. Improving Face Recognition Rate by Combining Eigenface Approach and Case-based Reasoning. Proceedings of the World congress on Engineering 2008, Vol. 1 WCE 2008, July, 2 4, 2008, London, U. K. 12 Jae young Choi, Yong Man Ro, and Konstantinos N. Plataniotis. Color Face Recognition for degraded Face Images. IEEE Transactions on Systems, Man and Cybernetics- Part-B Cybernetics, Vol. 39, No. 5, October, 2009. 13 Muriel Visani, Christophe Garcia, and Christophe Laurent. Comparing Robustness of Two-Dimensional PCA and Eigenfaces for Face Recognition. A. Campilho, M. Karnel (Eds.) ICIAR, 2004 LNCS 3212, pp. 717-724, 2004. 14 Dmitry O. Gorodnichy, Video-based framework for face recognition in video. Second workshop of Face Processing in Video (FPiV05) in Proceedings of second Canadian Conference on Computer and Robot Vision (CRV05). Pp. 330-338. Victoria British Columbia, Canada. May 9-11, 2005. ISBN 0-7695-2319-6. NRC 48216. 15 B. S.Venkatesh, S. Palanivel and B. Yegnanarayana, Face detection and Recognition in an image sequence using Eigen ediginess. Department of Comptuer science and Engineering, IIT, Madras 16 Aamer S.S., Mohamed, Ying Weng, Stan S Ipson, jianmin Jiang, Face detection based on skin color in image by Neural Networks 17 Shu-Fai Wong and Kwan-Yee Kenneth Wong. Fast Face Detection Using QuadTree Based Color Analysis and Support Vector Verification. ICIAR 2004, LNCS 3212, pp. 676-683, 2004. Springer-Velag Berlin Heidelberg 2004. 18 Federico Matta, Jean-Luc Dugelay. Video face recognition a Physiological and behavioural multimodal approach. Eurecom Institute 2229 Route des Cretes, 06904 Sophia Antipolis, France. _p
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Hi!
I'm Loren!

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