We propose a method for indoor versus outdoor scene classification using a probabilistic neural network (PNN). The scene is initially segmented (unsupervised) using fuzzy C-means clustering (FCM) and features based on color, texture, and shape are extracted from each of the image segments. The image is thus represented by a feature set, with a separate feature vector for each image segment. As the number of segments differs from one scene to another, the feature set representation of the scene is of varying dimension. Therefore a modified PNN is used for classifying the variable dimension feature sets. The proposed technique is evaluated on two databases: IITM-SCID2 (scene classification image database) and that used by Payne and Singh in 2005. The performance of different feature combinations is compared using the modified PNN.
In this paper, a geometric method for estimating the face pose (roll and yaw angles) from a single uncalibrated view is presented. The symmetric structure of the human face is exploited by taking the mirror image (horizontal flip) of a test face image as a virtual second view. Facial feature point correspondences are established between the given test and its mirror image using an active appearance model. Thus, the face pose estimation problem is cast as a two-view rotation estimation problem. By using the bilateral symmetry, roll and yaw angles are estimated without the need for camera calibration. The proposed pose estimation method is evaluated on synthetic and natural face datasets, and the results are compared with an eigenspace-based method. It is shown that the proposed symmetry-based method shows performance that is comparable to the eigenspace-based method for both synthetic and real face image datasets.
Acquiring fingertip ECG (electrocardiogram) signal using dry contact electrodes is challenging due to the presence of noise and interference by EMG (electromyogram) potentials. In this paper, we propose a method for using the fingertip ECG signal for biometric authentication. The noisy segments of the signal are segmented out using a variance-based heuristic and the clean signal is used for subsequent processing. By applying baseline correction and band pass filtering, the filtered signal is used for beat feature extraction. The features are used to train a support vector machine (SVM) classifier. Experimental results are presented to show the optimum filter parameters and feature sets for best classification performance. The performance of the proposed method with the optimum parameters was evaluated on a public domain CYBHi dataset with 126 subjects and the beat level EER of 3.4% was obtained.
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