Abstract—Visual surveillance in dynamic scenes, especially for humans and vehicles, is currently one of the most active research topics in computer vision. It has a wide spectrum of promising applications, including access control in special areas, human identification at a distance, crowd flux statistics and congestion analysis, detection of anomalous behaviors, and interactive surveillance using multiple cameras, etc. In general, the processing framework of visual surveillance in dynamic scenes includes the following stages: modeling of environments, detection of motion, classification of moving objects, tracking, understanding and description of behaviors, human identification, and fusion of data from multiple cameras. We review recent developments and general strategies of all these stages. Finally, we analyze possible research directions, e.g., occlusion handling, a combination of twoand three-dimensional tracking, a combination of motion analysis and biometrics, anomaly detection and behavior prediction, content-based retrieval of surveillance videos, behavior understanding and natural language description, fusion of information from multiple sensors, and remote surveillance. Index Terms—Behavior understanding and description, fusion of data from multiple cameras, motion detection, personal identification, tracking, visual surveillance.

We present a component-based method and two global methods for face recognition and evaluate them with respect to robustness against pose changes. In the component system we first locate facial components, extract them and combine them into a single feature vector which is classified by a Support Vector Machine (SVM). The two global systems recognize faces by classifying a single feature vector consisting of the gray values of the whole face image. In the first global system we trained a single SVM classifier for each person in the database. The second system consists of sets of viewpoint-specific SVM classifiers and involves clustering during training. We performed extensive tests on a database which included faces rotated up to about 40° in depth. The component system clearly outperformed both global systems on all tests.

Face recognition is a rapidly growing research area due to increasing demands for security in commercial and law enforcement applications. This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional (2D) images in the visual and infrared (IR) spectra. Face recognition systems based on visual images have reached a significant level of maturity with some practical success. However, the performance of visual face recognition may degrade under poor illumination conditions or for subjects of various skin colors. IR imagery represents a viable alternative to visible imaging in the search for a robust and practical identification system. While visual face recognition systems perform relatively reliably under controlled illumination conditions, thermal IR face recognition systems are advantageous when there is no control over illumination or for detecting disguised faces. Face recognition using 3D images is another active area of face recognition, which provides robust face recognition with changes in pose. Recent research has also demonstrated that the fusion of different imaging modalities and spectral components can improve the overall performance of face recognition.

A new approach combining a simple local representation method with a k-nearest neighbours-based direct voting scheme is proposed for face recognition. This approach rises computational problems that we efectively solve through an approximate fast k-nearest neighbours search technique. Experimental results with the widely used Olivetti Research Ltd (ORL) face database are reported showing the effectiveness of the proposed approach.

In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting condition, where we can hardly know the strength, the directions, and the number of light sources. The proposed LTV model has the capability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition. The merit of this model is that neither does it require any lighting assumption nor does it need any training process. Besides, there is only one parameter which could be easily set. The LTV model is able to reach very high recognition rates on both Yale and CMU PIE face databases as well as on a face database containing 765 subjects under outdoor lighting conditions. Keywords: I.5.4.d Face and gesture recognition; I.5.4.m Signal processing; I.4 Image Processing and Computer Vision; I.5.2.c Pattern analysis;

Abstract—Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human ages. The aging process is determined by not only the person’s gene, but also many external factors, such as health, living style, living location, and weather conditions. Males and females may also age differently. The current age estimation performance is still not good enough for practical use and more effort has to be put into this research direction. In this paper, we introduce the age manifold learning scheme for extracting face aging features and design a locally adjusted robust regressor for learning and prediction of human ages. The novel approach improves the age estimation accuracy significantly over all previous methods. The merit of the proposed approaches for image-based age estimation is shown by extensive experiments on a large internal age database and the public available FG-NET database. Index Terms—Age manifold, human age estimation, locally adjusted robust regression, manifold learning, nonlinear regression, support vector machine (SVM), support vector regression (SVR). I.

In this paper, a new system for face recognition is proposed, based on Hidden Markov Models (HMMs) and wavelet coding. A sequence of overlapping sub-images is extracted from each face image, computing the wavelet coefficients for each of them. The whole sequence is then modelled by using Hidden Markov Models. The proposed method is compared with a DCT coefficients-based approach [9], showing comparable results. By using an accurate model selection procedure, we show that results proposed in [9] can be improved even more. The obtained results outperform all results presented in the literature on the Olivetti Research Laboratory (ORL) face database, reaching a recognition rate. These performances proves the suitability of HMMs to deal with the new JPEG2000 image compression standard. 1.

We propose to use the AdaBoost algorithm for face recognition. AdaBoost is a kind of large margin classifiers and is efficient for on-line learning. In order to adapt the AdaBoost algorithm to fast face recognition, the original Adaboost which uses all given features is compared with the boosting along feature dimensions. The comparable results assure the use of the latter, which is faster for classification. The AdaBoost is typically a classification between two classes. To solve the multi-class recognition problem, a majority voting (MV) strategy can be used to combine all the pairwise classification results. However, the number of pairwise comparisons �is huge, when the number of individuals is very large in the face database. We propose to use a constrained majority voting (CMV) strategy to largely reduce the number of pairwise comparisons, without losing the recognition accuracy. Experimental results on a large face database of 1079 faces of 137 individuals show the feasibility of our approach for fast face recognition. Keywords: Face recognition, large margin classifiers, AdaBoost, constrained majority voting (CMV), principal component analysis (PCA). 1.

We present a new algorithm for illumination normalization and uneven background correction in images, utilizing the recently proposed TV+L 1 model: minimizing the total variation of the output cartoon while subject to an L 1-norm fidelity term. We give intuitive proofs of its main advantages, including the well-known edge preserving capability, minimal signal distortion, and scale-dependent but intensity-independent foreground extraction. We then propose a novel TV-based quotient image model (TVQI) for illumination normalization, an important preprocessing for face recognition under different lighting conditions. Using this model, we achieve 100 % face recognition rate on Yale face database B if the reference images are under good lighting condition and 99.45 % if not. These results, compared to the average 65 % recognition rate of the quotient image model and the average 95 % recognition rate of the more recent self quotient image model, show a clear improvement. In addition, this model requires no training data, no assumption on the light source, and no alignment between different images for illumination normalization. We also present the results of the related applications- uneven background correction for cDNA microarray films and digital microscope images. We believe the proposed works can serve important roles in the related fields. 4 1.

Example-based learning for computer vision can be difficult when a large number of examples to represent each pattern or object class is not available. In such situations, learning from a small number of samples is of practical value. To study this issue, the task of face expression recognition with a small number of training images of each expression is considered. A new technique based on linear programming for both feature selection and classifier training is introduced. A pairwise framework for feature selection, instead of using all classes simultaneously, is presented. Experimental results compare the method with three others: a simplified Bayes classifier, support vector machine, and AdaBoost. Finally, each algorithm is analyzed and a new categorization of these algorithms is given, especially for learning from examples in the small sample case.