A State-of-the-Art Neural Network for Robust Face Verification in .NET

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3.6.2 A State-of-the-Art Neural Network for Robust Face Verification
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Bengio et al. [13], propose the use of skin color as an additional feature for robust face veri cation. This new feature set is tested on a benchmark database, namely XM2VTS, using a simple discriminant arti cial neural network. The bounding box for the face is computed using the coordinates of the located eyes, assuming perfect face detection. The face is then cropped and the extracted subimage is down-sized to a 30 40 image. After enhancement and smoothing, the face then becomes a feature vector of dimension 1200. The skin color pixels are ltered from the subimage corresponding to the extracted face, using a look-up table of skin color pixels. For better results, the face bounding box should thus avoid as much hair as possible. The histograms for RGB pixel components are calculated. These histograms are characteristic to a speci c person and also can be used as discriminant among different people. The illumination during image acquisition is controlled. For each color channel, a histogram is built using 32 discrete bins. Hence, the feature vector produced by the concatenation of the histograms (R, G, and B) has 96 components. For veri cation, the authors choose multilayer perceptrons (MLP). For each client, the MLP is trained to classify the input as the given client or an imposter. The input to the MLP is a feature vector extracted from the face image with or without skin color. The MLP is trained using both client and imposter images. The database used for this purpose is the multimodal XM2VTS database, and its associated experimental protocol is the Lausanne Protocol. The XM2VTS database contains synchronized image and speech data recorded on 295 subjects during four sessions taken at one-month intervals. On each session, two recordings were made, each consisting of a speech shot and a head rotation shot. The 295 subjects were divided into a set of 200 clients, 25 evaluation impostors, and 70 test impostors.
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For experimenting and comparison, the authors compared an MLP using 1200 inputs corresponding to the downsized 30 40 gray-scale face images and an MLP using 1296 inputs corresponding to the same face image and skin color. For each client model the training database consisted of four images of the client and four images of the imposter training set. The database was enlarged by rotating and scaling these images. These training sets were later divided into three subsets, one for training, one for validation, and a third as a test set. A 90-hidden-unit MLP was the chosen architecture. The authors conclude that the results using the skin color information achieve state-of-the art results and have enhanced performance.
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3.6.3 Face Recognition: A Convolution Neural Network Approach
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Back et al. [14] present a hybrid neural network solution. The system combines local image sampling, a self-organizing map neural network, and a convolutional neural network. The authors present results using the Karhunen Loeve transform in place of the self-organizing map and a multilayer perceptron in place of the convolutional network. The convolutional network extracts larger features in a hierarchical set of layers. The authors are interested in face recognition with varying facial detail, expression, pose, and so on, and do not consider invariance to high degree of scaling or rotation. Initially, local sampling of the image is done. The authors evaluate two methods to perform local sampling: 1. The rst method is to create a vector from a local window on the image using intensity values at each point in the window. 2. The second method creates a representation of the local sample by forming a vector out of the intensity of the center pixel and the difference in intensity between the center pixel and all the other pixels within the square window. A self-organizing map (SOM) is trained on the vectors from the previous stage. Back et al. [14] also experiment with replacing the SOM with Karhunen Loeve transform. The same window as in the rst step is stepped over all of the images in the test, and training sets and the generated vectors are passed through the SOM at each step, thereby creating new training and test sets in the output space created by selforganizing map. A convolutional neural network is trained on the newly created training set. The network consists of a set of layers, each of which contains one or more planes. Multiple planes are used to detect multiple features. Back et al. [14] also experimented with training a standard multilayer perceptron for comparison, but it resulted in poorer performance since the MLP does not have inbuilt variance to minor translation and local deformation. With respect to computational complexity, the SOM takes considerable time to train. But the system can be extended to cover new classes without retraining. It also took considerable time to train the convolutional network. To overcome this issue,
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