In this paper, the effects of feature selection and feature normalization to the performance of a local appearance based face recognition scheme are presented. From the local features that are extracted using block-based discrete cosine transform, three feature sets are derived. These local feature vectors are normalized in two different ways; by making them unit norm and by dividing each coefficient to its standard deviation that is learned from the training set. The input test face images are then classified using four different distance measures: L1 norm, L2 norm, cosine angle and covariance between feature vectors. Extensive experiments have been conducted on the AR and CMU PIE face databases. The experimental results show the importance of using appropriate feature sets and doing normalization on the feature vector. 1.

In this paper, a local appearance based face recognition algorithm is proposed. In the proposed algorithm local information is extracted using block-based discrete cosine transform. Obtained local features are combined both at the feature level and at the decision level. The performance of the proposed algorithm is tested on the Yale and CMU PIE face databases, and the obtained results show significant improvement over the holistic approaches. 1.

In this paper we present the experimental results of a generic local appearance based face representation approach obtained from the first and fourth experiments of the Face Recognition Grand Challenge (FRGC) version 1 data. The introduced representation approach is compared with the baseline system with the standard distance metrics of L1 norm, L2 norm and cosine angle. The experimental results show that the proposed local appearance based approach provides better and more stable results than the baseline system-holistic Eigenfaces- approach. Since 1990s, with the introduction of Eigenfaces

� This work was performed while the authors were visiting Microsoft Research Asia.

Abstract. In this article we propose a novel Wavelet Packet Decomposition (WPD)-based modification of the classical Principal Component Analysis (PCA)-based face recognition method. The proposed modification allows to use PCA-based face recognition with a large number of training images and perform training much faster than using the traditional PCA-based method. The proposed method was tested with a database containing photographies of 423 persons and achieved 82–89 % first one recognition rate. These results are close to that achieved by the classical PCAbased method (83–90%). Key words: face recognition, PCA, Wavelet Packet Decomposition, WPD. 1.

Abstract. 3D models play an important role in many industrial applications. Therefore semantic processing for the purposes of comparing, cataloging and archiving shapes is a major concern. Most previous work considers comparisons based on the object’s overall geometry or in a reference frame which is computed from the object’s geometry alone, disregarding its context. There are also approaches which propose to match feature points to perform context alignment to better analyze a single element. In complex assemblies created in a CAD system, however, the parts (components and layers) are often explicitly marked and named and therefore the geometric context is evident. In this paper we show how this can be exploited with the aid of Knowledge Management tools to establish accurate frames of reference where the individual shapes can be better analyzed. 1

Abstract-- In this paper, we investigate the face recognition problem via energy histogram of the DCT coefficients. Several issues related to the recognition performance are discussed, in particular the issue of histogram bin sizes and feature sets. In addition, we propose a technique for selecting the classification threshold incrementally. Experimentation was conducted on the Yale face database and results indicated that the threshold obtained via the proposed technique provides a balanced recognition in term of precision and recall. Furthermore, it demonstrated that the energy histogram algorithm outperformed the well-known Eigenface algorithm. I.

In this paper we investigate the face recognition problem via the overlapping energy histogram of the DCT coefficients. Particularly, we investigate some important issues relating to the recognition performance, such as the issue of selecting threshold and the number of bins. These selection methods utilise information obtained from the training dataset. Experimentation is conducted on the Yale face database and results indicate that the proposed parameter selection methods perform well in selecting the threshold and number of bins. Furthermore, we show that the proposed overlapping energy histogram approach outperforms the Eigenfaces, 2DPCA and energy histogram significantly. 1

Abstract—When the feature dimension is larger than the number of samples the small sample-size problem occurs. There is great concern about it within the face recognition community. We point out that optimizing the Fisher index in linear discriminant analysis does not necessarily give the best performance for a face recognition system. We propose a new regularization scheme. The proposed method is evaluated using the Olivetti Research Laboratory database, the Yale database, and the Feret database. Index Terms—Face recognition, optimization, regularized discriminant analysis (RDA), small sample-size problem. I.

Abstract—Patch-based face recognition is a recent method which uses the idea of analyzing face images locally, in order to reduce the effects of illumination changes and partial occlusions. Feature fusion and decision fusion are two distinct ways to make use of the extracted local features. Apart from the well-known decision fusion methods, a novel approach for calculating weights for the weighted sum rule is proposed in this paper. Improvements in recognition accuracies are shown and superiority of decision fusion over feature fusion is advocated. In the challenging AR database, we obtain significantly better results using decision fusion as compared to conventional methods and feature fusion methods by using validation accuracy weighting scheme and nearest-neighbor discriminant analysis dimension reduction method. Keywords-face recognition, patch-based face recognition, decision fusion, linear combiner training. I.