2,036 research outputs found

    Using Bezier Curve analysis in context of Expression Analysis

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    Affective computing is an area of research under increasing demand in the field of computer vision. Expression analysis, in particular, is a topic that has been undergoing research for many years. In this paper, an algorithm for expression determination and analysis is performed for the detection of seven expressions: sadness, anger, happiness, neutral, fear, disgust and surprise. First, the 68 landmarks of the face are detected and the face is realigned and warped to obtain a new image. Next, feature extraction is performed using LPQ. We then use a dimensionality reduction algorithm followed by a dual RBF-SVM and Adaboost classification algorithm to find the interest points in the features extracted. We then plot bezier curves on the regions of interest obtained. The curves are then used as the input to a CNN and this determines the facial expression. The results showed the algorithm to be extremely successfu

    RSD-DOG : A New Image Descriptor based on Second Order Derivatives

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    International audienceThis paper introduces the new and powerful image patch de-scriptor based on second order image statistics/derivatives. Here, the image patch is treated as a 3D surface with intensity being the 3rd dimension. The considered 3D surface has a rich set of second order fea-tures/statistics such as ridges, valleys, cliffs and so on, that can be easily captured by using the difference of rotating semi Gaussian filters. The originality of this method is based on successfully combining the response of the directional filters with that of the Difference of Gaussian (DOG) approach. The obtained descriptor shows a good discriminative power when dealing with the variations in illumination, scale, rotation, blur, viewpoint and compression. The experiments on image matching, demonstrates the advantage of the obtained descriptor when compared to its first order counterparts such as SIFT, DAISY, GLOH, GIST and LIDRIC

    Illumination tolerance in facial recognition

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    In this research work, five different preprocessing techniques were experimented with two different classifiers to find the best match for preprocessor + classifier combination to built an illumination tolerant face recognition system. Hence, a face recognition system is proposed based on illumination normalization techniques and linear subspace model using two distance metrics on three challenging, yet interesting databases. The databases are CAS PEAL database, the Extended Yale B database, and the AT&T database. The research takes the form of experimentation and analysis in which five illumination normalization techniques were compared and analyzed using two different distance metrics. The performances and execution times of the various techniques were recorded and measured for accuracy and efficiency. The illumination normalization techniques were Gamma Intensity Correction (GIC), discrete Cosine Transform (DCT), Histogram Remapping using Normal distribution (HRN), Histogram Remapping using Log-normal distribution (HRL), and Anisotropic Smoothing technique (AS). The linear subspace models utilized were principal component analysis (PCA) and Linear Discriminant Analysis (LDA). The two distance metrics were Euclidean and Cosine distance. The result showed that for databases with both illumination (shadows), and lighting (over-exposure) variations like the CAS PEAL database the Histogram remapping technique with normal distribution produced excellent result when the cosine distance is used as the classifier. The result indicated 65% recognition rate in 15.8 ms/img. Alternatively for databases consisting of pure illumination variation, like the extended Yale B database, the Gamma Intensity Correction (GIC) merged with the Euclidean distance metric gave the most accurate result with 95.4% recognition accuracy in 1ms/img. It was further gathered from the set of experiments that the cosine distance produces more accurate result compared to the Euclidean distance metric. However the Euclidean distance is faster than the cosine distance in all the experiments conducted

    GRASP News Volume 9, Number 1

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    A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory

    Supervised Material Classification in Oblique Aerial Imagery Using Gabor Filter Features

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    RIT\u27s Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool allows modeling of real world scenes to create synthetic imagery for sensor design and analysis, trade studies, algorithm validation, and training image analysts. To increase model construction speed, and the diversity and size of synthetic scenes which can be generated it is desirable to automatically segment real world imagery into different material types and import a material classmap into DIRSIG. This work contributes a methodology based on standard texture recognition techniques to supervised classification of material types in oblique aerial imagery. Oblique imagery provides many challenges for texture recognition due to illumination changes with view angle, projective distortions, occlusions and self shadowing. It is shown that features derived from a set of rotationally invariant bandpass filters fused with color channel information can provide supervised classification accuracies up to 70% with minimal training data
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