Person Location Service on the Planetary Sensor Network

This paper gives a prototype application which can provide a person location service on the IrisNet. Two crucial technologies face detection and face recognition underpinning such image and video data mining service are explained. For the face detection, authors use 4 types of simple rectangles as features, Adaboost as the learning algorithm to select the important features for classification, and finally generate a cascade of classifiers which is extremely fast on the face detection task. As for the face recognition, the authors develop Adaptive Principle Components Analysis (APCA) to improve the robustness of principal Components Analysis (PCA) to nuisance factors such as lighting and expression. APCA also can recognize faces from single face which is suitable in a data mining situatio

Robust Face Recognition in Rotated Eigen Space

Face recognition is a very complex classification problem due to nuisance variations in different conditions. Most face recognition approaches either assume constant lighting condition or standard facial expressions, thus cannot deal with both kinds of variations simultaneously. Principal Component Analysis (PCA) cannot handle complex pattern variations such as illumination and expression. Adaptive PCA rotates eigenspace to extract more representative features thus improving the performance. In this paper, we present a way to extract various sets of features by different eigenspace rotations and propose a method to fuse these features to generate nonorthogonal mappings for face recognition. The proposed method is tested on the Asian Face Database with 856 images from 107 subjects with 5 lighting conditions and 4 expressions. We register only one normally lit neutral face image and test on the remaining face images with variations. Experiments show a 95% classification accuracy and a 20% reduction in error rate. This illustrates that the fused features can provide significantly improved pattern classification

Face Recognition with One Sample Image per Class

There are two main approaches for face recognition with variations in lighting conditions. One is to represent images with features that are insensitive to illumination in the first place. The other main approach is to construct a linear subspace for every class under the different lighting conditions. Both of these techniques are successfully applied to some extent in face recognition, but it is hard to extend them for recognition with variant facial expressions. It is observed that features insensitive to illumination are highly sensitive to expression variations, which result in face recognition with changes in both lighting conditions and expressions a difficult task. We propose a new method called Affine Principal Components Analysis in an attempt to solve both of these problems. This method extract features to construct a subspace for face representation and warps this space to achieve better class separation. The proposed technique is evaluated using face databases with both variable lighting and facial expressions. We achieve more than 90% accuracy for face recognition by using only one sample image per class

Facial expressions recognition based on dimensionality reduction techniques

Interest in image retrieval has increased in large part due to the rapid growth of the World Wide Web. The traditional text based search and retrieval has its own limitations and hence we move to a facial expressions images are search and retrieval system. In this paper we present a facial expression retrieval system that takes an image as the input query and retrieves images based on image content. Face recognition system is recognizing based on dimensionality reduction derived image features. Facial expressions recognition is the application of computer vision to the image retrieval problem. In this recognition context might refer colours, shapes, textures, or any other information that can be derived from the image itself

Robust Face Recognition for Data Mining

While the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Yet these forms of multimedia data are becoming increasingly prevalent on the internet and intranets as bandwidth rapidly increases due to continuing advances in computing hardware and consumer demand. An emerging major problem is the lack of accurate and efficient tools to query these multimedia data directly, so we are usually forced to rely on available metadata such as manual labeling. Currently the most effective way to label data to allow for searching of multimedia archives is for humans to physically review the material. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them - and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21st century, postproduction of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the internet backbone

A survey of face detection, extraction and recognition

The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important

On Intelligent Surveillance Systems and Face Recognition for Mass Transport Security

We describe a project to trial and develop enhanced surveillance technologies for public safety. A key technology is robust recognition of faces from low-resolution CCTV footage where there may be as few as 12 pixels between the eyes. Current commercial face recognition systems require 60-90 pixels between the eyes as well as tightly controlled image capture conditions. Our group has thus concentrated on fundamental face recognition issues such as robustness to low resolution and image capture conditions as required for uncontrolled CCTV surveillance. In this paper, we propose a fast multi-class pattern classification approach to enhance PCA and FLD methods for 2D face recognition under changes in pose, illumination, and expression. The method first finds the optimal weights of features pairwise and constructs a feature chain in order to determine the weights for all features. Computational load of the proposed approach is extremely low by design, in order to facilitate usage in automated surveillance. The method is evaluated on PIE, FERET, and Asian Face databases, with the results showing that the method performs remarkably well compared to several benchmark appearance-based methods. Moreover, the method can reliably recognise faces with large pose angles from just one gallery image