International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 2 Issue 2, February – 2018, Pages: 12-17 www.ijeais.org/ijaer 12 Color-Texture Feature in The Facial Detection And Calculation T.Augusty Chandija Lincy, Francis Xavier Engineering College, Tirunelveli-627003, Tamilnadu, INDIA. Email: lincythavasiraj@gmail.com Mrs. S. Murine Sharmili Francis Xavier Engineering College, Tirunelveli-627003, Tamilnadu, INDIA. Email : sharmili0904@gmail.coM Abstract: The object is detected, track and it is an important in computer vision type which also includes activity of recognition, automotive of safety and surveillance under. Face identification is an easier and simplest task for humans, but not it is so easy for computers. It have been recognized as a most complex and challenging dare in the field and application of computer sight because of large intra-class differences which is caused by the differences in facial aspect, light source and express maters. Such difference shows in the facial distribution to at highly nonlinearity and complexier in any expance that is straight to the original image space vector. Face identification is the process of identify the more than one human faces in image and video. This plays an vital part in many biometric, security and surveillance systems, as well as image and video indexing systems. Face detection is a difficult task in image analysis which has each day and more applications. We propose this method by using viola-Jones algorithm and user interface. The viola-Jones algorithm is the first ever real time face detection. The integral image for feature computation. Adaboost for feature selection and attentional cascade for efficient computational resource allocation. Since the viola-Jones algorithm typically gives multiple detections, a processing step which is in the post method is also known to diminish in detect discharge using a robustness argument. A user link interfaces which is a graphe items display in more than one windows containing controls called components that enable a user to act the interactive tasks. Keywords: color-texture, detection, calculation, facial 1. INTRODUCTION With the greater in increase of mathematical power & available of coming sensing netwok. Computer are now become one of extra and more than intelligent. Face detection is the first step method to the full facial analys algorithm which includes face align, face relight, face model, ace recognization and the head pose in the track, face verify method, facial expression tracking/recognition and lots of more. This is the computers can recognize face well and good will they begin to truly understand people„s recognization and intent methods. In the Face detection it shows the presence and absence of a face in an image pixel, by distinguish the face from all other patterns in present in the scene. It also shows the requirement of appropriate modeling and segmentation. It should take into account that the sources of variation of facial appearance. It is very difficult to handle. Face detection methodologies classified on the basis of the image information used to aid in detection color geometric shape or motion information In the violajones algorithm it review the algorithm in maters, it starts with the image shows in the feature for the classification of the method. It has 3 ideas of viola-Jones Algorithm is first Introduction of a new image representation called the Integral Image And then Simple and efficient classifier which is built using the Adaboost learning algorithm and then method for combine small classifier in a " surge" which makes background regions of the image pixel to be quickly stoped.In the Features and Integral Image the easy features used are comparable with of Haar basis functions are2rectangle feature is the difference between the sum of the pixels within two rectangular regions.3-rectangle feature is the sum within two outside rectangles subtracted from the sum in a centre rectangle.4 rectangle feature is the difference between diagonal pairs of rectangle. It is to compensate the effect of different lighting conditions, all the images should be mean and variance normalized beforehand. Those images with variance lower than one, having little information of interest in the first place, are left out of consideration. The Feature Selection with Adaboost is to select the features and to train the classifiers. The 1st 2 features selected is the Adaboost for the factor of face detect can be easily interpreted. In the First feature to the region of the eyes is often darker than the region of the nose and cheeks and in the Second feature is to the eyes are darker than the bridge of the nose. In the Cascaded classifier it is Smaller, and therefore more efficient, classifiers can be constructed which reject many of the negative sub windows while detecting almost all positive instance which is Simpler classifiers are used to reject the majority of Subwindows and the more complex classifiers are called upon to achieve low false positive rates. In the Position and scale invariance is the final detector is scanned across the images at multiple scales and locations and the Scaling is achieved by scaling the detector itself, rather than scaling the image (features can be evaluated at any scale with the same cost) finally the detector is also scanned across location, by shifting the window some number of pixels then the Preprocessing is to Sub-windows were variance normalized during training to minimize the effect of different lighting conditions and the same is done during detection as well.Atlast in the Final postprocessing it International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 2 Issue 2, February – 2018, Pages: 12-17 www.ijeais.org/ijaer 13 is useful to postprocess the detected sub-windows in order to combine overlapping detections into a single detection. 2. PROBLEM DESCRIPTION AND PREVIOUS WORK. There are many algorithms used to detect the face. They are Local Binary Pattern (LBP)The local binary pattern (LBP) technique is very effective to describe the image texture features. LBP has advantages such as high speed computation and rotation invariance, which facilitates the broad usage in the fields of image retrieval, texture examination, face recognition, image segmentation, etc. Following this LBP was correctfully applied to the identification of moving objects as act the background subtraction. In LBP, in each and every pixel in the image is to be assigned a texture value of texture, which can be finally combined with value of target for tracking thermographic and video of monochromatic. The major uniform LBP patterns are used to recognize the key points in the target region and then form a mask for joint color-texture feature selection. In the AdaBoost method for Face identification there will be Booting and boosting occurs is an approach to machine learning based on the idea of creating a highly accurate prediction rule by combining many relatively weak and incorrect rules. In this algorithm it is the first practical boosting algorithm, and one of the mostly wider used method and studied method, with applications in numerous issue. Using boosting algorithm to train a classifier which is capable of processing images rapidly while having high detection rates. It is more repellent of to over fitting than many machine method of learning algorithm type, it is repeatedly sensitive to noisy data and outliers. It is called to be adaptive because of it uses multiple the act of repeating to generates a one and only composite strong learner type. It creates the very good strong learner by iteratively adding very less learners.. The outcome of this classifier that has high accuracy than that of the weak classifiers. In the Robust Real-Time Objection Detection this seems to be the first article where Viola-Jones present the coherent set of ideas that constitute the fundamentals of their face detection algorithm. This algorithm only finds frontal upright faces .In the Neural Network of Based Face Detection it is an image pyramid is calculated by orders to detect faces at multiple scales of given input. A non-variable which is size of subwindows is pass through the each images of pixel in the pyramid. In its first method this algorithm it only detects frontal of upright face. 3. PROBLEM IDENTIFICATION AND SOLUTION The method used to Viola-Jones method is for face detection with MATLAB program is first they create a detector object using Viola-Jones algorithm and then they take the image from the video and they detect the features and to annotate the detected features BLOCK DIAGRAM Fig 1. Block Diagram of Capture of face To detect a face or a particular feature on the faces of people following steps are used in MATLAB: 1. Definition and set the cascade material identificator using the erection. Detection is equal to vision, cascade, object and detector. It shows a systems object identifier that detects only objects which uses the Viola-Jones algorithm. Its Classifier Model property which controls the which type of object is to detect at the time. By the default method that the detector is alignment to detect the face which in the time. In the Next the Call step method with all the input image J, the cascade object detector object, detector, points PTS and any optional properties. Use the steps in which the obligatory with input image J and the selected cascaded objects identifier object, and any optional properties to perform detection. BBOX is equal to step (detector, I).It returns BBOX with an M with the by with the 4 matrix defining M bounding boxes containing the identified objects. This International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 2 Issue 2, February – 2018, Pages: 12-17 www.ijeais.org/ijaer 14 method perform the method of multi-scale object identification of the input image I. Each row of the output of the matrix BBOX contains a 4-element vector as x as width and y as height which specifies in pixels, the upper-left corner and size of a bounding box. Input image J must be a grayscale or true colour (RGB) image. Then the insertObjectAnnotation (I,‟rectangle‟,Position, Label). It also inserts the rectangles and corresponding labels with the location which indicated by the position of the matrix. The position which give as input must be an M-by-4 matrix, where each row (M) specifies a rectangle as a 4-element vector which is x as width and y is height. The elements x and y indicate the upper-left corner of the rectangles, and the width, height specifies the size. Checking the device ID Format supported by camera International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 2 Issue 2, February – 2018, Pages: 12-17 www.ijeais.org/ijaer 15 Tool operations Click on Start Camera to initialize Click on Count Face for video display and Starts face recognition in front of the camera To close/end the face recognition through the camera Display the total count of the faces in front of the camera 4. RESULT AND DISCUSSION AFTER RUNNING THE PROGRAM FIGURE SHOWS THAT NUMBER OF FACE COUNT IS ZERO. In the above block the program runs and detect there is no face before the camera.so the face count is zero FINAL OUTPUT OF THE PROGRAM: International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 2 Issue 2, February – 2018, Pages: 12-17 www.ijeais.org/ijaer 16 FIGURE SHOWS THAT NUMBER OF FACE COUNT IS THREE. In the above block the program runs and detect there is a face before the camera.so the face count is three. 5. CONCLUSION We present an extensive survey of object identification method for detection. It is already to see face detection techniques be increasingly used in real-world applications and products. For instance, most digital cameras today have built-in face detectors, which can help the camera to do better auto-focusing and auto-exposure. It has less ideal for faces at different poses. Another interesting idea to improve face detection performance is to consider the context formation. Human face is very much like linked with the other parts of the body, and those other body parts can provides a very good and strong cues of faces. In outer environments which has very low variations and in adapt would mostly bring more very extremely and very high improvement in the detection the faces. Othertan in other domains such as speech denotion and handwrites recognitions is to where adapt has been indispensable, adaptation in all visual objects detection has been received slightly little attraction of attention. REFERENCES 1. N. Muthukumaran, 'Analyzing Throughput of MANET with Reduced Packet Loss', Wireless Personal Communications, Print-ISSN: 0929-6212, E-ISSN: 1572-834X, Vol. 97, No. 1, pp. 565-578, November 2017, SPRINGER. 2. N. Muthukumaran, Mrs R.Sonya, Dr.Rajashekhara and Chitra V, 'Computation of Optimum ATC Using Generator Participation Factor in Deregulated System', International Journal of Advanced Research Trends in Engineering and Technology, Vol. 4, No. 1, pp. 8-11, January 2017. 3. Muthukumaran. N and Ravi. R, 'Hardware Implementation of Architecture Techniques for Fast Efficient loss less Image Compression System', Wireless Personal Communications, Volume. 90, No. 3, pp. 1291-1315, October 2016, SPRINGER. 4. Muthukumaran. N and Ravi. R, 'The Performance Analysis of Fast Efficient Lossless Satellite Image Compression and Decompression for Wavelet Based Algorithm', Wireless Personal Communications, Volume. 81, No. 2, pp. 839-859, March 2015, SPRINGER. 5. Muthukumaran. N and Manoj Kumar. B, 'Design of Low power high Speed CASCADED Double Tail Comparator', International Journal of Advanced Research in Biology Engineering Science and Technology, Vol. 2, No. 4, pp.18-22, June 2016. 6. Muthukumaran. N and Keziah. J, 'Design of K Band Transmitting Antenna for Harbor Surveillance Radar Application', International Journal on Applications in Electrical and Electronics Engineering, Vol. 2, No. 5, pp. 16-20, May 2016. 7. Muthukumaran. N and Ravi. R, 'VLSI Implementations of Compressive Image Acquisition using Block Based Compression Algorithm', International Arab Journal of Information Technology, vol. 12, no. 4, pp. 333339, July 2015. 8. Muthukumaran. N and Ravi. R, 'Simulation Based VLSI Implementation of Fast Efficient Lossless Image Compression System using Simplified Adjusted Binary Code & Golumb Rice Code', World Academy of Science, Engineering and Technology, Volume. 8, No. 9, pp.1603-1606, 2014. 9. Muthukumaran. N, Ravi. R and Ruban Kingston. M, 'A Novel Scheme of CMOS VCO Design with reduce number of Transistors using 180nm CAD Tool', International Journal of Applied Engineering Research, Volume. 10, No. 14, pp. 11934-11938, 2015. International Journal of Academic Engineering Research (IJAER) ISSN: 2000-001X Vol. 2 Issue 2, February – 2018, Pages: 12-17 www.ijeais.org/ijaer 17 10. Muthukumaran. N and Ravi. R, 'Design and analysis of VLSI based FELICS Algorithm for lossless Image Compression', International Journal of Advanced Research in Technology, Vol. 2, No. 3, pp. 115-119, March 2012. 11. Akhil. M.S and Muthukumaran. N, 'Design of Optimizing Adders for Low Power Digital Signal Processing', International Journal of Engineering Research and Applications, Vol. 5, pp. 59-65, March 2014. 12. Muthukumaran. N and Ravi. R, 'Design and analysis of VLSI based FELICS Algorithm for lossless Image Compression', International Journal of Communications and Engineering, Vol. 6, Issue. 2, pp. 98-104, March 2012. 13. Muthukumaran. N and Ravi. R, 'Design and analysis of VLSI based FELICS Algorithm for lossless Image Compression', International Journal of Advanced Research in Technology, Vol. 2, Issue 3, pp. 115-119, March 2012. T. Augusty Chandija Lincy was born in Tirunelveli, Tamilnadu, India, in 1994. She received the B.E (Electronics and Communication Engineering) from Francis Xavier Engineering College ,Tirunelveli. She currently pursuing her Post Graduate programme in Communication System from Francis Xavier Engineering College. Her major research interests are Digital and Image Processing and Embedded system. S. Murine Sharmili was born in Coimbatore, Tamilnadu, India. She received the B.E(Electronics and Communication Engineering) from Government College of Engineering, Tirunelveli, India in 2010 and doing the M.E(Communication Systems) in Francis Xavier Engineering College, Tirunelveli, India in 2018.