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In this paper the analysis of gray level corner detection has been carried out. Performances of various cornerness measures are discussed with respect to four performances of robustness: detection, localization, stability and complexity. We have analyzed the interior dierential features of the image surface of these cornerness measures. This paper presents a new approach called gradient-direction corner detector for the corner detection which is developed from the popular Plessey corner detection. The gradient-direction corner detector is based on the measure of the gradient module of the image gradient direction and the constraints of the false corner response suppression. Ó

Recognition of natural emotions from human faces is an interesting topic with a wide range of potential applications, such as human-computer interaction, automated tutoring systems, image and video retrieval, smart environments, and driver warning systems. Traditionally, facial emotion recognition systems have been evaluated on laboratory controlled data, which is not representative of the environment faced in real-world applications. To robustly recognize the facial emotions in real-world natural situations, this paper proposes an approach called extreme sparse learning, which has the ability to jointly learn a dictionary (set of basis) and a nonlinear classification model. The proposed approach combines the discriminative power of extreme learning machine with the reconstruction property of sparse representation to enable accurate classification when presented with noisy signals and imperfect data recorded in natural settings. In addition, this paper presents a new local spatio-temporal descriptor that is distinctive and pose-invariant. The proposed framework is able to achieve the state-of-the-art recognition accuracy on both acted and spontaneous facial emotion databases.