The goal of this work is the identification of humans based on motion data in the form of natural hand gestures. In this paper, the identification problem is formulated as classification with classes corresponding to persons' identities, based on recorded signals of performed gestures. The identification performance is examined with a database of twenty-two natural hand gestures recorded with two types of hardware and three state-of-art classifiers: Linear Discrimination Analysis (LDA), Support Vector machines (SVM) and k-Nearest Neighbour (k-NN). Results show that natural hand gestures allow for an effective human classification.
Despite their limitations, linear classifiers @cite_26 proved to produce good results for many applications, including face recognition @cite_34 and speech detection @cite_33 . In @cite_9 LDA is used for the estimation of consistent parameters to three model standard types of violin bow strokes. Authors show that such gestures can be effectively presented in the bi-dimensional space. In @cite_36 , the LDA classifier was compared with neural networks (NN) and focused time delay neural networks (TDNN) for gesture recognition based on data from a 3-axis accelerometer. LDA gave similar results to the NN approach, and the TDNN technique, though computationally more complex, achieved better performance. An analysis of LDA and the PCA algorithm, with a discussion about their performance for the purpose of object recognition is provided in @cite_1 . SVM and k-NN classifiers were used in @cite_18 for the purpose of visual category recognition. A comparison of the effectiveness of these method is classification of human gait patterns is provided in @cite_4 .
@cite_18 @cite_26 @cite_4 @cite_33 @cite_36 @cite_9 @cite_1 @cite_34
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We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to prototype examples of categories. This approach is quite flexible, and permits recognition based on color, texture, and particularly shape, in a homogeneous framework. While nearest neighbor classifiers are natural in this setting, they suffer from the problem of high variance (in bias-variance decomposition) in the case of limited sampling. Alternatively, one could use support vector machines but they involve time-consuming optimization and computation of pairwise distances. We propose a hybrid of these two methods which deals naturally with the multiclass setting, has reasonable computational complexity both in training and at run time, and yields excellent results in practice. The basic idea is to find close neighbors to a query sample and train a local support vector machine that preserves the distance function on the collection of neighbors. Our method can be applied to large, multiclass data sets for which it outperforms nearest neighbor and support vector machines, and remains efficient when the problem becomes intractable for support vector machines. A wide variety of distance functions can be used and our experiments show state-of-the-art performance on a number of benchmark data sets for shape and texture classification (MNIST, USPS, CUReT) and object recognition (Caltech- 101). On Caltech-101 we achieved a correct classification rate of 59.05 (±0.56 ) at 15 training images per class, and 66.23 (±0.48 ) at 30 training images. Information fusion offers a promising solution to the development of a high performance classification system. In this paper multiple gait components such as spatial, temporal and wavelet are fused for enhancing the classification rate. Initially background modeling is done from a video sequence and the foreground moving objects in the individual frames are segmented using the background subtraction algorithm. Then gait representing features are extracted for training and testing the multi_class k Nearest Neighbor models (kNN) and multi_class support vector machine models (SVM). We have successfully achieved our objective with only two gait cycles and our experimental results demonstrate that the classification ability of SVM is better than kNN. The proposed system is evaluated using side view videos of NLPR database. In speech recognition, speech non-speech detection must be robust to,noise. In the paper, a method for speech non-speech detection using a linear discriminant analysis (LDA) applied to mel frequency cepstrum coefficients (MFCC) is presented. The energy is the most discriminant parameter between noise and speech. But with this single parameter, the speech non-speech detection system detects too many noise segments. The LDA applied to MFCC and the associated test reduces the detection of noise segments. This new algorithm is compared to the one based on signal to noise ratio (Mauuary and Monne, 1993). We used Fisher linear discriminant analysis (LDA), static neural networks (NN), and focused time delay neural networks (TDNN) for gesture recognition. Gestures were collected in form of acceleration signals along three axes from six participants. A sports watch containing a 3-axis accelerometer, was worn by the users, who performed four gestures. Each gesture was performed for ten seconds, at the speed of one gesture per second. User-dependent and user-independent k-fold cross validations were carried out to measure the classifier performance. Using first and second order statistical descriptors of acceleration signals from validation datasets, LDA and NN classifiers were able to recognize the gestures at an average rate of 86 and 97 (user-dependent) and 89 and 85 (user-independent), respectively. TDNNs proved to be the best, achieving near perfect classification rates both for user-dependent and user-independent scenarios, while operating directly on the acceleration signals alleviating the need for explicit feature extraction. We developed an ”augmented violin”, i.e. an acoustic instrument with added gesture capture capabilities to control electronic processes. We report here gesture analysis we performed on three different bow strokes, Detache, Martele and Spiccato, using this augmented violin. Different features based on velocity and acceleration were considered. A linear discriminant analysis has been performed to estimate a minimum number of pertinent features necessary to model these bow stroke classes. We found that the maximum and minimum accelerations of a given stroke were efficient to parameterize the different bow stroke types, as well as differences in dynamics playing. Recognition rates were estimated using a kNN method with various training sets. We finally discuss that bow stroke recognition allows to relate the gesture data to music notation, while a bow stroke continuous parameterization can be related to continuous sound characteristics. In the context of the appearance-based paradigm for object recognition, it is generally believed that algorithms based on LDA (linear discriminant analysis) are superior to those based on PCA (principal components analysis). In this communication, we show that this is not always the case. We present our case first by using intuitively plausible arguments and, then, by showing actual results on a face database. Our overall conclusion is that when the training data set is small, PCA can outperform LDA and, also, that PCA is less sensitive to different training data sets. Linear discriminant analysis (LDA) is a popular feature extraction technique for face recognition. However, It often suffers from the small sample size problem when dealing with the high dimensional face data. Fisherface and null space LDA (N-LDA) are two conventional approaches to address this problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. In this paper, by analyzing different overfitting problems for the two kinds of LDA classifiers, we propose an approach using random subspace and bagging to improve them respectively. By random sampling on feature vector and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed. The two kinds of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. We also apply this approach to the integration of multiple features. A robust face recognition system integrating shape, texture and Gabor responses is finally developed.