Constructing a fuzzy grammar for syntactic face detection

This paper presents a structural face detection system. The proposed system consists of three stages; pre-processing, face-components extraction, and final decision-making. In the first stage, image conversion, colour operation, image restoration, and image enhancement are carried out. Face components are extracted in the second stage. A face model is defined, and a fuzzy grammar composed of octal chain codes is used to represent each of the seven face components. The practical limitations of this representation are considered. Structural components are detected, and the possibility degree that the extracted component is a real face component is determined. In the last stage, a commonsense knowledge base is employed for final evaluation. The detected face components and their corresponding possibility degrees allow the human face knowledge base to locate faces in the image and generate a membership degree for that face within the face class. The experimental results obtained using this method are presented

A fast and accurate algorithm for facial feature segmentation

Abstrac

Face recognition using Hidden Markov Models

This dissertation introduces work on face recognition using a novel technique based on Hidden Markov Models (HMMs). Through the integration of a priori structural knowledge with statistical information, HMMs can be used successfully to encode face features. The results reported are obtained using a database of images of 40 subjects, with 5 training images and 5 test images for each. It is shown how standard one-dimensional HMMs in the shape of top-bottom models can be parameterised, yielding successful recognition rates of up to around 85%. The insights gained from top-bottom models are extended to pseudo two-dimensional HMMs, which offer a better and more flexible model, that describes some of the twodimensional dependencies missed by the standard one-dimensional model. It is shown how pseudo two-dimensional HMMs can be implemented, yielding successful recognition rates of up to around 95%. The performance of the HMMs is compared with the Eigenface approach and various domain and resolution experiments are also carried out. Finally, the performance of the HMM is evaluated in a fully automated system, where database images are cropped automatically

Interactive-time vision--face recognition as a visual behavior

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Architecture, 1991.Includes bibliographical references (leaves 107-115).by Matthew Alan Turk.Ph.D

Block-level discrete cosine transform coefficients for autonomic face recognition

This dissertation presents a novel method of autonomic face recognition based on the recently proposed biologically plausible network of networks (NoN) model of information processing. The NoN model is based on locally parallel and globally coordinated transformations. In the NoN architecture, the neurons or computational units form distributed networks, which themselves link to form larger networks. In the general case, an n-level hierarchy of nested distributed networks is constructed. This models the structures in the cerebral cortex described by Mountcastle and the architecture based on that proposed for information processing by Sutton. In the implementation proposed in the dissertation, the image is processed by a nested family of locally operating networks along with a hierarchically superior network that classifies the information from each of the local networks. The implementation of this approach helps obtain sensitivity to the contrast sensitivity function (CSF) in the middle of the spectrum, as is true for the human vision system. The input images are divided into blocks to define the local regions of processing. The two-dimensional Discrete Cosine Transform (DCT), a spatial frequency transform, is used to transform the data into the frequency domain. Thereafter, statistical operators that calculate various functions of spatial frequency in the block are used to produce a block-level DCT coefficient. The image is now transformed into a variable length vector that is trained with respect to the data set. The classification was done by the use of a backpropagation neural network. The proposed method yields excellent results on a benchmark database. The results of the experiments yielded a maximum of 98.5% recognition accuracy and an average of 97.4% recognition accuracy. An advanced version of the method where the local processing is done on offset blocks has also been developed. This has validated the NoN approach and further research using local processing as well as more advanced global operators is likely to yield even better results

On the presence and functional significance of sympathetic premotor neurons with collateralized spinal axons in the rat

KEY POINTS: Spinally-projecting neurons of the rostral ventrolateral medulla (RVLM) determine sympathetic outflow to different territories of the body. Previous studies suggest the existence of RVLM neurons with distinct functional classes, such as neurons that target sympathetic nerves bound for functionally-similar tissue types (e.g. muscle vasculature). The existence of RVLM neurons with more general actions had not been critically tested. Using viral tracing, we show that a significant minority of RVLM neurons send axon collaterals to disparate spinal segments (T2 and T10 ). Furthermore, optogenetic activation of sympathetic premotor neurons projecting to lumbar spinal segments also produced activation of sympathetic nerves from rostral spinal segments that innervate functionally diverse tissues (heart and forelimb muscle). These findings suggest the existence of individual RVLM neurons for which the axons branch to drive sympathetic preganglionic neurons of more than one functional class and may be able to produce global changes in sympathetic activity. ABSTRACT: We investigate the extent of spinal axon collateralization of rat rostral ventrolateral medulla (RVLM) sympathetic premotor neurons and its functional consequences. In anatomical tracing experiments, two recombinant herpes viral vectors with retrograde tropism and expressing different fluorophores were injected into the intermediolateral column at upper thoracic and lower thoracic levels. Histological analysis revealed that ∼21% of RVLM bulbospinal neurons were retrogradely labelled by both vectors, indicating substantial axonal collateralization to disparate spinal segments. In functional experiments, another virus with retrograde tropism, a canine adenovirus expressing Cre recombinase, was injected into the left intermediolateral horn around the thoracolumbar junction, whereas a Cre-dependent viral vector encoding Channelrhodopsin2 under LoxP control was injected into the ipsilateral RVLM. In subsequent terminal experiments, blue laser light (473 nm × 20 ms pulses at 10 mW) was used to activate RVLM neurons that had been transduced by both vectors. Stimulus-locked activation, at appropriate latencies, was recorded in the following pairs of sympathetic nerves: forelimb and hindlimb muscle sympathetic fibres, as well as cardiac and either hindlimb muscle or lumbar sympathetic nerves. The latter result demonstrates that axon collaterals of lumbar-projecting RVLM neurons project to, and excite, both functionally similar (forelimb and hindlimb muscle) and functionally dissimilar (lumbar and cardiac) preganglionic neurons. Taken together, these findings show that the axons of a significant proportion of RVLM neurons collateralise widely within the spinal cord, and that they may excite preganglionic neurons of more than one functional class

Pose-invariant face recognition using real and virtual views

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 173-184).by David James Beymer.Ph.D

Shape classification: towards a mathematical description of the face

Recent advances in biostereometric techniques have led to the quick and easy acquisition of 3D data for facial and other biological surfaces. This has led facial surgeons to express dissatisfaction with landmark-based methods for analysing the shape of the face which use only a small part of the data available, and to seek a method for analysing the face which maximizes the use of this extensive data set. Scientists working in the field of computer vision have developed a variety of methods for the analysis and description of 2D and 3D shape. These methods are reviewed and an approach, based on differential geometry, is selected for the description of facial shape. For each data point, the Gaussian and mean curvatures of the surface are calculated. The performance of three algorithms for computing these curvatures are evaluated for mathematically generated standard 3D objects and for 3D data obtained from an optical surface scanner. Using the signs of these curvatures, the face is classified into eight 'fundamental surface types' - each of which has an intuitive perceptual meaning. The robustness of the resulting surface type description to errors in the data is determined together with its repeatability. Three methods for comparing two surface type descriptions are presented and illustrated for average male and average female faces. Thus a quantitative description of facial change, or differences between individual's faces, is achieved. The possible application of artificial intelligence techniques to automate this comparison is discussed. The sensitivity of the description to global and local changes to the data, made by mathematical functions, is investigated. Examples are given of the application of this method for describing facial changes made by facial reconstructive surgery and implications for defining a basis for facial aesthetics using shape are discussed. It is also applied to investigate the role played by the shape of the surface in facial recognition