A dynamic texture based approach to recognition of facial actions and their temporal models

In this work, we propose a dynamic texture-based approach to the recognition of facial Action Units (AUs, atomic facial gestures) and their temporal models (i.e., sequences of temporal segments: neutral, onset, apex, and offset) in near-frontal-view face videos. Two approaches to modeling the dynamics and the appearance in the face region of an input video are compared: an extended version of Motion History Images and a novel method based on Nonrigid Registration using Free-Form Deformations (FFDs). The extracted motion representation is used to derive motion orientation histogram descriptors in both the spatial and temporal domain. Per AU, a combination of discriminative, frame-based GentleBoost ensemble learners and dynamic, generative Hidden Markov Models detects the presence of the AU in question and its temporal segments in an input image sequence. When tested for recognition of all 27 lower and upper face AUs, occurring alone or in combination in 264 sequences from the MMI facial expression database, the proposed method achieved an average event recognition accuracy of 89.2 percent for the MHI method and 94.3 percent for the FFD method. The generalization performance of the FFD method has been tested using the Cohn-Kanade database. Finally, we also explored the performance on spontaneous expressions in the Sensitive Artificial Listener data set

Drowziness Detection System using Image Processing

There have been a lot of approaches in the detection of drowsiness. The parameters taken into consideration were the eye opening window, the number of blinks during a time period, no. of yawns etc. there are about 12 facial features that can be determined by the camera mounted on the circuit board. The parameter considered here is only the window of eye opening. In addition to the 12 parameters, the head motion was also taken into consideration which, in turn, contributed to the improvement of the accuracy of the measurement. Driver Drowsiness is one of the real reasons for mishaps on the planet. In this undertaking I plan to build up a model of drowsiness recognition framework. This framework meets expectations by observing the eyes of the driver and sounding a caution when he/she is tired. The framework so outlined is a non-nosy continuous checking framework. The need is on enhancing the security of the driver without being prominent. In this venture the eye flicker of the driver is recognized. In the event that the drivers’ eyes stay shut for more than a certain duration of time, the driver is said to be languid and an alert is sounded. The programming for this is done in matlab using image acquisition tool

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 125

This special bibliography lists 323 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1974

Robust Face Representation and Recognition Under Low Resolution and Difficult Lighting Conditions

This dissertation focuses on different aspects of face image analysis for accurate face recognition under low resolution and poor lighting conditions. A novel resolution enhancement technique is proposed for enhancing a low resolution face image into a high resolution image for better visualization and improved feature extraction, especially in a video surveillance environment. This method performs kernel regression and component feature learning in local neighborhood of the face images. It uses directional Fourier phase feature component to adaptively lean the regression kernel based on local covariance to estimate the high resolution image. For each patch in the neighborhood, four directional variances are estimated to adapt the interpolated pixels. A Modified Local Binary Pattern (MLBP) methodology for feature extraction is proposed to obtain robust face recognition under varying lighting conditions. Original LBP operator compares pixels in a local neighborhood with the center pixel and converts the resultant binary string to 8-bit integer value. So, it is less effective under difficult lighting conditions where variation between pixels is negligible. The proposed MLBP uses a two stage encoding procedure which is more robust in detecting this variation in a local patch. A novel dimensionality reduction technique called Marginality Preserving Embedding (MPE) is also proposed for enhancing the face recognition accuracy. Unlike Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which project data in a global sense, MPE seeks for a local structure in the manifold. This is similar to other subspace learning techniques but the difference with other manifold learning is that MPE preserves marginality in local reconstruction. Hence it provides better representation in low dimensional space and achieves lower error rates in face recognition. Two new concepts for robust face recognition are also presented in this dissertation. In the first approach, a neural network is used for training the system where input vectors are created by measuring distance from each input to its class mean. In the second approach, half-face symmetry is used, realizing the fact that the face images may contain various expressions such as open/close eye, open/close mouth etc., and classify the top half and bottom half separately and finally fuse the two results. By performing experiments on several standard face datasets, improved results were observed in all the new proposed methodologies. Research is progressing in developing a unified approach for the extraction of features suitable for accurate face recognition in a long range video sequence in complex environments

Recognition of License Plates and Optical Nerve Pattern Detection Using Hough Transform

The global technique of detection of the features is Hough transform used in image processing, computer vision and image analysis. The detection of prominent line of the object under consideration is the main purpose of the Hough transform which is carried out by the process of voting. The first part of this work is the use of Hough transform as feature vector, tested on Indian license plate system, having font of UK standard and UK standard 3D, which has ten slots for characters and numbers.So tensub images are obtained.These sub images are fed to Hough transform and Hough peaks to extract the Hough peaks information. First two Hough peaks are taken into account for the recognition purposes. The edge detection along with image rotation is also used prior to the implementation of Hough transform in order to get the edges of the gray scale image. Further, the image rotation angle is varied; the superior results are taken under consideration. The second part of this work makes the use of Hough transform and Hough peaks, for examining the optical nerve patterns of eye. An available database for RIM-one is used to serve the purpose. The optical nerve pattern is unique for every human being and remains almost unchanged throughout the life time. So the purpose is to detect the change in the pattern report the abnormality, to make automatic system so capable that they can replace the experts of that field. For this detection purpose Hough Transform and Hough Peaks are used and the fact that these nerve patterns are unique in every sense is confirmed