This paper presents a new method for face verification for vision applications. There are many approaches to detect and track a face in a sequence of images; however, the high computations of image algorithms, as well as, face detection and head tracking failures under unrestricted environments remain to be a difficult problem. We present a robust algorithm that improves face detection and tracking in video sequences by using geometrical facial information and a recurrent neural network verifier. Two types of neural networks are proposed for face detection verification. A new method, a three-face reference model (TFRM), and its advantages, such as, allowing for a better match for face verification, will be discussed in this paper.
In this paper we propose a new encryption technique for wireless sensor networks (3DSSec). Although RC4 is more susceptible to cryptanalysis attacks than RC5, the proposed encryption scheme has implemented RC4 in a way that boosts security when used in sensor networks and still allows a respectable performance/security trade off. The proposed encryption scheme is a good choice for Mica2 network sensors because it is based on an encryption algorithm that performs very well on Atmega128 platforms and has very modest memory demands. The encryption system seems a very good balance between performance and security given the limits of network sensors.
In this paper, we describe an algorithm which can automatically recognize human gesture in a sequence of natural video image by utilizing two dimensional features extracted from bodily region of the images. In the algorithm, we first construct a gesture model space by analyzing the statistical information of sample images with principle component analysis method. And then, input images are compared to the model and individually symbolized to one part of the model space. In the last step, the symbolized images are recognized with HMM as one of model gestures. The feature of our method is to use a combination of partial and global information of two-dimensional abstract bodily motion, consequently it is very convenient to apply to real world situation and the recognition results are very robust.
We present a crossbreed feature-based head tracking technique in natural and unspecified environment. Kalman filter is a famous estimation technique in many areas to predict the route of moving object. We tested and developed a Kalman filter to track unpredictable and fast moving objects. Depth information could generate robust tracking result that is little affected by background texture and color. However this is also limited by selected conditions like distance, accuracy of stereo camera, and object occlusion at same distance, etc. To overcome these restrictions, we combined multiple features together into single tracking system that does largely depend on depth feature. We consider multi people environment with rapid walking path.
In this paper, we present a region-based scalable coding technique that can be used in interactive image/video communications. This method has a capability of near lossless coding for a specific region of interest (ROI), while the rest of the region is coded with high quality lossy codec. There are many potential applications of the region-based scalable video coding method in the areas of interactive communications such as storm tracking, wild fire and air traffic monitoring using satellite still images and near real time video sequences. It enables the server/client system to reduce data traffic across the networks while the quality of a specific area of interest chosen for each client's needs is still satisfied. We tested this technique by applying it to still images and traffic monitoring image sequences. The results consistently show high level of performance.
This paper presents our approach to using a stereo camera to obtain 3-D image data to be used to improve existing lip boundary detection techniques. We show that depth information as provided by our approach can be used to significantly improve boundary detection systems. Our system detects the face and mouth area in the image by using color, geometric location, and additional depth information for the face. Initially, color and depth information can be used to localize the face. Then we can determine the lip region from the intensity information and the detected eye locations. The system has successfully been used to extract approximate lip regions using RGB color information of the mouth area. Merely using color information is not robust because the quality of the results may vary depending on light conditions, background, and the human race. To overcome this problem, we used a stereo camera to obtain 3-D facial images. 3-D data constructed from the depth information along with color information can provide more accurate lip boundary detection results as compared to color only based techniques.
This paper presents a region-based scalable coding technique that can be used in interactive transmission of images over networks. This method has a capability of near lossless coding for a specific region of interest (ROI), while the rest of the region is coded with a high quality lossy codec. The enhancement layers add refinement to the quality of the images that have been reconstructed using the basic layer of the intra frame. The proposed coding technique uses multiple quantizers with thresholds (QT) for layering and it creates a bit plane for each layer of both the intra and residual frames. The bit plane is then partitioned into sets of small areas to be coded independently. Run-length and entropy coding are applied to each of the sets to provide scalability for the entire image sets resulting in high picture quality in the end user-specified ROI. We tested this technique by applying it to various test image sequences and we consistently achieved a high level of performance.
This paper presents a new, scalable coding technique that can be used in interactive image/video communications over the Internet. The proposed technique generates a fully embedded bit stream that provides scalability with high quality for the whole image and it can be used to implement region based coding as well. The embedded bit stream is comprised of a basic layer and many enhancement layers. The enhancement layers add refinement to the quality of the image that has been reconstructed using the basic layer. The proposed coding technique uses multiple quantizers with thresholds (QT) for layering and it creates a bit plane for each layer. The bit plane is then partitioned into sets of small areas to be coded independently. Run length and entropy coding are applied to each of the sets to provide scalability for the entire image resulting in high picture quality in the user-specific area of interest (ROI). We tested this technique by applying it to various test images and the results consistently show high level of performance.