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Face Detection Through Neural Analysis - rakesh nath - 08-16-2017 Face Detection Through Neural Analysis
Akhil G S, Gibu George S4 Mechanical Engineering, Muslim Association College Of Engineering, Venjaramoodu [attachment=10143] Abstract The aim of this paper is to implement an effective system to locate upright frontal faces on monochromatic images with use of a neural network-based classifier. In this paper, A new approach to reduce the computation time taken by fast neural nets for the searching process is presented. The principle of divide and conquer strategy is applied through image decomposition. Each image is divided into small in size sub - images and then each one is tested separately using a fast neural network. Compared to conventional and fast neural networks, experimental results show that a speed up ratio is achieved when applying this technique to locate human faces in automatically in cluttered scenes. Furthermore, faster face detection is obtained by using parallel processing techniques to test the resulted sub-images at the same time using the same number of fast neural networks. Moreover, the problem of sub image centring and normalization in the Fourier space is solved Introduction An artificial neural network (ANN), also called a simulated neural network (SNN) or commonly just neural network (NN) is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. A NN is a massively parallel distributed processing system made up of highly interconnected neural computing elements. It has the ability to learn and thereby acquire Knowledge and make it available for use. The Neural Network is the imitation of the Central nervous system By applying Sobel masks on the given image the system retrieves an appropriate edge image. Since at each iteration, only 20x20 pixel faces are intended to be detected, the system removes all edges that are quite large with the assumption that they represent large objects which belong to the images background or large faces which, would be detected at latter iterations. Of course there exists the risk of removing objects which are in fact faces, however experiments have shown that this case just can be treated as an exception. By involving this module the system avoids analysing all possible 20x20 pixel windows at each location (pixel after pixel), what is a quite time consuming task. The number of remained 20x20 pixel windows needed to be individually analysed by the detector in the latter steps is only about 20 percent of the whole. Bootstrapping Generating a training set for the SVM /neural network is a challenging task because of the difficulty in placing characteristic non-face images in a the training set. To get a representative sample of face images is not much of a problem; however, to Choose the right combination of non- face images from the immensely large set of such images, is a complicated task. For this purpose, after each training session, non-faces incorrectly detected as faces are placed in the training set for the next session. This bootstrap method overcomes the problem of using a huge set of non face images in the training set, many of which may not influence the training. database and a database of Indian faces generated here. In each image to be placed in the training set the eyes, nose and left, right and centre of the mouth were marked. With these markings, the face was transformed into a 20x20 window with the marked features at predetermined positions. The training set was subsequently enhanced with bootstrapping of scenery and false-detected images. To make the system somewhat invariant to changes such as rotation of the face random transformations (rotation by 15 degrees, mirroring) were Initially, for negative samples, random images were created and added to applied to images in the training set. The last used training set (including bootstrapping) had 8982 input vectors. Multi-layer networks use a variety of learning techniques, 4 the most popular being back propagation. Here the output values are compared with the correct answer to compute the value of some predefined error-function. By various techniques the error is then fed back through the network. Using this information, the algorithm adjusts the weights of each connection in order to reduce the value of the error function by some small amount. After repeating this process for a sufficiently large number of training cycles the network will usually converge to some state where the error of the calculations is small. In this case one says that the network has learned a certain target function. To adjust weights properly one applies a general method for non-linear optimization. task that is called gradient descent. For this, the derivative of the error function with respect to the network weights is calculated and the eights are then changed such that the error decreases (thus going downhill on the surface of the error function). For this reason back-propagation can only be applied on networks with differentiable activation functions. Network Structure This implementation is a crude version of the system described in [rowley98]. Arbitration amongst multiple networks and the size of the training set used was significantly smaller and this is not implemented. The neural network is a two-layer (one hidden, one output) feed-forward network. There are 400 input neurons, 26 hidden neurons and 1 output neurons. Each hidden neuron is not connected to ALL the input neurons Results For Standard Pictures Of Other Species Here we have shown some images of animal faces to see if the network learnt to recognize faces in general (two eyes, a nose and a mouth) or was able to detect something unique about human faces. Do note that none of these animal faces were in the training set. Some interesting results obtained were: The application screenshots above) didn t draw rectangle around the chimp, so it didn t think it was a face. However, when inspected more closely, we say that this chimp and some others too had a network output quite close to 0.5 (the demarcating limit we used between a face and a non face). This dog s face was detected by the network. The region after all does have two eyes; the fur of the dog is dark n the middle which makes it appear somewhat like a nose. However, many other dog faces were categorically rejected by the system. |