Proceedings ArticleDOI
Human face recognition using neural networks
TLDR
A simple technique for identification of human faces in cluttered scenes based on neural nets based on Fourier descriptors, which results in reduction of computational complexity and thus decreasing the time and memory needed during the testing of an image.Abstract:
Automatic recognition of human faces is a significant problem in the development and application of pattern recognition. We introduce a simple technique for identification of human faces in cluttered scenes based on neural nets. In the detection phase, neural nets are used to test whether a window of 20/spl times/20 pixels contains a face or not. A major difficulty in the learning process comes from the large database required for face/nonface images. We solve this problem by dividing these data into two groups. Such a division results in reduction of computational complexity and thus decreasing the time and memory needed during the testing of an image. For the recognition phase, feature measurements are made through Fourier descriptors. Such features are used as input to the neural classifier for training and recognition of ten human faces. Simulation results for the proposed algorithm show a good performance during testing.read more
Citations
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BookDOI
Recent Advances in Face Recognition
TL;DR: After this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work.
Proceedings ArticleDOI
Partial face recognition using radial basis function networks
TL;DR: A face recognition system that uses partial face images (for example, eye, nose, and ear images) for input data based on using radial basis function (RBF) networks, which are far superior for the face recognition task.
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Centroid tracking based dynamic hand gesture recognition using discrete Hidden Markov Models
TL;DR: Centroid tracking of hand gestures is introduced that captures and retains the time sequence information for feature extraction and simplifies the classification of dynamic gestures as movement in time helps efficient classification without burdensome processing.
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Pedestrian detection based on gradient and texture feature integration
TL;DR: This paper extracted the histogram of oriented gradients feature and local binary pattern feature from the original images respectively and K-singular value decomposition was used to extract sparse representation features from the HOG and LBP features.
Proceedings ArticleDOI
Face recognition using a fuzzy-Gaussian neural network
TL;DR: A new version of Chen and Teng's fuzzy neural network, which is modified from an identifier into a neurofuzzy classifier called fuzzy-Gaussian neural network (FGNN), is presented and a significant advantage of the proposed FGNN over FP is deduced.
References
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Face recognition: features versus templates
Roberto Brunelli,Tomaso Poggio +1 more
TL;DR: Two new algorithms for computer recognition of human faces, one based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second based on almost-gray-level template matching are presented.
Proceedings ArticleDOI
The FERET evaluation methodology for face-recognition algorithms
TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.