7 results
Search Results
2. IMPACT OF FULL RANK PRINCIPAL COMPONENT ANALYSIS ON CLASSIFICATION ALGORITHMS FOR FACE RECOGNITION.
- Author
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SONG, FENGXI, YOU, JANE, ZHANG, DAVID, and XU, YONG
- Subjects
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RANKING (Statistics) , *PRINCIPAL components analysis , *ALGORITHMS , *HUMAN facial recognition software , *IMAGE processing , *SUPPORT vector machines , *DATABASES , *PATTERN recognition systems - Abstract
Full rank principal component analysis (FR-PCA) is a special form of principal component analysis (PCA) which retains all nonzero components of PCA. Generally speaking, it is hard to estimate how the accuracy of a classifier will change after data are compressed by PCA. However, this paper reveals an interesting fact that the transformation by FR-PCA does not change the accuracy of many well-known classification algorithms. It predicates that people can safely use FR-PCA as a preprocessing tool to compress high-dimensional data without deteriorating the accuracies of these classifiers. The main contribution of the paper is that it theoretically proves that the transformation by FR-PCA does not change accuracies of the k nearest neighbor, the minimum distance, support vector machine, large margin linear projection, and maximum scatter difference classifiers. In addition, through extensive experimental studies conducted on several benchmark face image databases, this paper demonstrates that FR-PCA can greatly promote the efficiencies of above-mentioned five classification algorithms in appearance-based face recognition. [ABSTRACT FROM AUTHOR]
- Published
- 2012
- Full Text
- View/download PDF
3. VEHICLE TYPE AND MAKE RECOGNITION BY COMBINED FEATURES AND ROTATION FOREST ENSEMBLE.
- Author
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ZHANG, BAILING and ZHOU, YIFAN
- Subjects
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AUTOMOBILE identification , *IMAGE processing , *TELEVISION in security systems , *INTELLIGENT transportation systems , *COMPARATIVE studies , *PRINCIPAL components analysis , *FEATURE extraction , *OPTICAL resolution , *HISTOGRAMS - Abstract
Vehicle type/make recognition based on images captured by surveillance cameras is a challenging task in intelligent transportation system and automatic surveillance. In this paper, we comparatively studied two feature extraction methods for image description, i.e. a new multiresolution analysis tool called Fast Discrete Curvelet Transform and the pyramid histogram of oriented gradients (PHOG). Curvelet Transform has better directional and edge representation abilities than widely used wavelet transform, which is particularly appropriate for the description of images rich with edges. PHOG represents the local shape by a histogram of edge orientations computed for each image sub-region, quantized into a number of bins, thus has the ascendency in its description of more discriminating information. A composite feature description from PHOG and Curvelet can further increase the accuracy of classification by taking their complementary information. We also investigated the applicability of the Rotation Forest (RF) ensemble method for vehicle classification based on the combined features. The RF ensemble contains a set of base multilayer perceptrons which are trained using principal component analysis to rotate the original axes of combined features of vehicle images. The class label is assigned by the ensemble via majority voting. Experimental results using more than 600 images from 21 makes of cars/vans show the effectiveness of the proposed approach. The composite feature is better than any single feature in the classification accuracy and the ensemble model produces better performance compared to any of the individual neural network base classifier. With a moderate ensemble size of 20, the Rotation Forest ensembles offers a classification rate close to 96.5%, exhibiting promising potentials for real-life applications. [ABSTRACT FROM AUTHOR]
- Published
- 2012
- Full Text
- View/download PDF
4. FACE RECOGNITION USING CURVELET-BASED TWO-DIMENSIONAL PRINCIPLE COMPONENT ANALYSIS.
- Author
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ZHANG, YAN, YU, BIN, and GU, HAI-MING
- Subjects
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HUMAN facial recognition software , *TWO-dimensional models , *PRINCIPAL components analysis , *COVARIANCE matrices , *IMAGE processing , *COMPARATIVE studies , *SUPPORT vector machines , *ALGORITHMS - Abstract
The task of face recognition has been actively researched in recent years because of its many applications in various domains. This paper presents a robust face recognition system using curvelet-based two-dimensional principle component analysis (2D PCA) to address the problem of human face recognition from still images. 2D PCA has advantages over PCA in evaluating the covariance matrix accurately and time complexity. Inspired by the attractive attributes of curvelets in catching the edge singularities with very few coefficients in a non-adaptive manner, we introduce the scheme of decomposing images into curvelet subbands and applying 2D PCA to create a representative feature set. Experiments were designed with different implementations of each module using standard testing database. We experimented with changing the illumination normalization procedure; comparing the baseline PCA-based method with the proposed scheme; studying effects on algorithm performance of k-nearest neighbor (kNN) classifier and Support Vector Machine (SVM) classifier in the classification process; also we experimented with different databases such as FERET, etc. High accuracy rate were achieved by the proposed scheme through a comparative study. [ABSTRACT FROM AUTHOR]
- Published
- 2012
- Full Text
- View/download PDF
5. FACE RECOGNITION USING HYBRID APPROACH.
- Author
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RANI, J. SHEEBA
- Subjects
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HUMAN facial recognition software , *PRINCIPAL components analysis , *FEATURE extraction , *FUZZY integrals , *IMAGE processing , *RADIAL basis functions , *PERFORMANCE evaluation - Abstract
In this paper, a hybrid feature extraction technique using 2D principal component analysis (2DPCA) and discrete orthogonal Krawtchouk moment (KM) are used to extract the global and local features from the face. Ensemble of RBF classifiers are used to classify the image. Decision-level fusion is done using fuzzy integral to generate more accurate classification than each of the constituent classifiers. The proposed system is evaluated using ORL and YALE databases. Experimental results show that the combination of global and local features promotes the system performance. The fusion of multiple RBFs using fuzzy integral performed better as compared to conventional aggregation rules. [ABSTRACT FROM AUTHOR]
- Published
- 2012
- Full Text
- View/download PDF
6. DECISION-LEVEL FUSION OF PCA AND LDA-BASED FACE RECOGNITION ALGORITHMS.
- Author
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MARCIALIS, GIAN LUCA and ROLI, FABIO
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PATTERN recognition systems , *ALGORITHMS , *PRINCIPAL components analysis , *IMAGE processing , *STATISTICAL correlation - Abstract
In this paper, a face recognition system based on the fusion of two well-known appearance-based algorithms, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), is proposed. Fusion is performed at the decision-level, that is, the outputs of the individual face recognition algorithms are combined. Two main benefits of such fusion are shown. First, the reduction of the dependence on the environmental conditions with respect to the best individual recognizer. Secondly, the overall performance improvement over the best individual recognizer. To this end, fusion is investigated under different environmental conditions, namely, "ideal" conditions, characterized by a very limited variability of environmental parameters, and "real" conditions with large variability of lighting and face expressions. [ABSTRACT FROM AUTHOR]
- Published
- 2006
- Full Text
- View/download PDF
7. COMPARATIVE ASSESSMENT OF CONTENT-BASED FACE IMAGE RETRIEVAL IN DIFFERENT COLOR SPACES.
- Author
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SHIH, PEICHUNG and LIU, CHENGJUN
- Subjects
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IMAGE processing , *IMAGE retrieval , *INFORMATION retrieval , *COLOR , *ALGORITHMS , *PRINCIPAL components analysis - Abstract
Content-based face image retrieval is concerned with computer retrieval of face images (of a given subject) based on the geometric or statistical features automatically derived from these images. It is well known that color spaces provide powerful information for image indexing and retrieval by means of color invariants, color histogram, color texture, etc. This paper assesses comparatively the performance of content-based face image retrieval in different color spaces using a standard algorithm, the Principal Component Analysis (PCA), which has become a popular algorithm in the face recognition community. In particular, we comparatively assess 12 color spaces (RGB, HSV, YUV, YCbCr, XYZ, YIQ, L*a*b*, U*V*W*, L*u*v*, I1I2I3, HSI, and rgb) by evaluating seven color configurations for every single color space. A color configuration is defined by an individual or a combination of color component images. Take the RGB color space as an example, possible color configurations are R, G, B, RG, RB, GB and RGB. Experimental results using 600 FERET color images corresponding to 200 subjects and 456 FRGC (Face Recognition Grand Challenge) color images of 152 subjects show that some color configurations, such as YV in the YUV color space and YI in the YIQ color space, help improve face retrieval performance. [ABSTRACT FROM AUTHOR]
- Published
- 2005
- Full Text
- View/download PDF
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