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Facial expression, a significant way of nonverbal communication, effectively conveys humans' mental state, emotions and intentions. Understanding of emotions through these expressions is an easy task for human beings. However, when it comes to Human Computer Interface (HCI), it is a developing research field that enables humans' to interact with computers through touch, voice, and gestures. Communication through expression in HCI is still a challenge. In addition, there are a variety of fields such as automotive, biometric, surveillance, teleconferencing etc. in which expression recognition system can be applied. In recent years, several different approaches have been proposed fr facial expression recognition, but most of them work only under definite environmental conditions. The proposed framework aims to recognize expressions (by analyzing the facial features extracted) based on the Active Shape Model (ASM).

In view of the continuous efforts by the automotive fraternity, for achieving traffic safety, detecting pedestrians from image/video has become an extensively researched topic in recent times. The task of detecting pedestrians in the urban traffic scene is complicated by the considerations involving pedestrian figure size, articulation, fast dynamics, background clutter, etc. A number of methods using different sensor technologies have been proposed in the past for the problem of pedestrian detection. To limit the scope, this paper reviews the techniques involved in day-time detection of pedestrians, with emphasis on the methods making use of a monocular visible-spectrum sensor. The paper achieves its objective by discussing the basic framework involved in detecting a pedestrian, while elaborating the requisites and the existing methodologies for implementing each stage of the basic framework.

Camera sensors that are made of silicon photodiodes and used in ordinary digital cameras are sensitive to visible as well as Near-Infrared (NIR) wavelength. However, since the human vision is sensitive only in the visible region, a hot mirror/infrared blocking filter is used in cameras. Certain complimentary attributes of NIR data are, therefore, lost in this process of image acquisition. However, RGB and NIR images are captured entirely in two different spectra/wavelengths; thus they retain different information. Since NIR and RGB images compromise complimentary information, we believe that this can be exploited for extracting better features, localization of objects of interest and in multi-modal fusion. In this paper, an attempt is made to estimate the NIR image from a given optical image. Using a normal optical camera and based on the compressed sensing framework, the NIR data estimation is formulated as an image recovery problem.