Titlebook: Human Recognition at a Distance in Video; Bir Bhanu,Ju Han Book 2010 Springer-Verlag London Limited 2010 Biometrics.Camera Networks.Face R

MAPLE

Discrimination Analysis for Model-Based Gait Recognitionor experimental performance predictions. Therefore, the discriminating power of gait as a feature for human recognition cannot be evaluated. In this chapter, a Bayesian based statistical analysis is performed to evaluate the discriminating power of static gait features (body part dimensions). Throug

cancer

Model-Based Human Recognition—2D and 3D Gaition where the model is in 3D and the data are in 2D. In addition, we present a 3D gait recognition approach where both the model and the gait data are in 3D. Detailed technical approaches and experimental results are presented for both 2D and 3D gait recognition.

獨(dú)特性

Fusion of Color/Infrared Video for Human Detectionregistration between color and thermal images is a challenging problem due to the difficulties associated with finding correspondence. However, moving people in a static scene provide cues to address this problem. We first propose a hierarchical scheme to automatically find the correspondence betwee

向外

Super-Resolution of Facial Images in Video at?a?Distance?a closed-loop approach for super-resolution of frontal faces, (b)?super-resolution of frontal faces with facial expressions, and (c)?super-resolution of side face images. The details of these technical approaches and experimental results are presented in this chapter.

改良

Evaluating Quality of Super-Resolved Face Imagesmeasures. The current quality measures aim to compare different fusion methods by assessing the quality of the fused images. They consider the information transferred between the super-resolved image and input images only. In this chapter, we propose an integrated objective quality evaluation measur

Intersect

Integrating Face Profile and Gait at?a?Distance has been found to be difficult in reality, especially when a person is walking at a distance in real-world outdoor conditions. For optimal performance, the system should use as much information as possible from the observations. In this chapter, we introduce a video based system which combines cues

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Daily-Value

Feature Level Fusion of Face and Gait at?a?Distanceon match score level fusion (Chap.?10), in this chapter, we present an approach that utilizes and integrates information from side face and gait at the feature level. The features of face and gait are obtained separately using principal component analysis (PCA) from enhanced side face image (ESFI) a

BILL

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